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Image retrieval: Ideas, influences, and trends of the new age

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Published:08 May 2008Publication History
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Abstract

We have witnessed great interest and a wealth of promise in content-based image retrieval as an emerging technology. While the last decade laid foundation to such promise, it also paved the way for a large number of new techniques and systems, got many new people involved, and triggered stronger association of weakly related fields. In this article, we survey almost 300 key theoretical and empirical contributions in the current decade related to image retrieval and automatic image annotation, and in the process discuss the spawning of related subfields. We also discuss significant challenges involved in the adaptation of existing image retrieval techniques to build systems that can be useful in the real world. In retrospect of what has been achieved so far, we also conjecture what the future may hold for image retrieval research.

References

  1. Aigrain, P., Zhang, H., and Petkovic, D. 1996. Content-based representation and retrieval of visual media: A review of the state-of-the-art. Multimed. Tools Appl. 3, 3, 179--202.Google ScholarGoogle ScholarCross RefCross Ref
  2. Airliners.Net. 2005. Airliners.net homepage. http://www.airliners.net.Google ScholarGoogle Scholar
  3. Alipr. 2006. Alipr homepage. http://www.alipr.com.Google ScholarGoogle Scholar
  4. Amores, J., Sebe, N., and Radeva, P. 2005. Fast spatial pattern discovery integrating boosting with constellations of contextual descriptors. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Amores, J., Sebe, N., and Radeva, P. 2006. Boosting the distance estimation: Application to the k-nearest neighbor classifier. Pattern Recogn. Lett. 27, 3, 201--209. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Amores, J., Sebe, N., Radeva, P., Gevers, T., and Smeulders, A. 2004. Boosting contextual information in content-based image retrieval. In Proceedings of the 6th ACM SIGMM International Workshop on Multimedia Information Retrieval (MIR) at the International Multimedia Conference. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Armitage, L. H. and Enser, P. G. B. 1997. Analysis of user need in image archives. J. Inf. Sci. 23, 4, 287--299.Google ScholarGoogle ScholarCross RefCross Ref
  8. Assfalg, J., Del Bimbo, A., and Pala, P. 2002. Three-Dimensional interfaces for querying by example in content-based image retrieval. IEEE Trans. Visualiz. Comput. Graphics 8, 4, 305--318. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Bar-Hillel, A., Hertz, T., Shental, N., and Weinshall, D. 2005. Learning a Mahalanobis metric from equivalence constraints. J. Mach. Learn. Res. 6, 937--965. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Barnard, K., Duygulu, P., Forsyth, D., de Freitas, N., Blei, D. M., and Jordan, M. I. 2003. Matching words and pictures. J. Mach. Learn. Res. 3, 1107--1135. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Barni, M., Pelagotti, A., and Piva, A. 2005. Image processing for the analysis and conservation of paintings: Opportunities and challenges. IEEE Signal Process. Mag. 22, 141--144.Google ScholarGoogle ScholarCross RefCross Ref
  12. Bartolini, I., Ciaccia, P., and Patella, M. 2005. Warp: Accurate retrieval of shapes using phase of Fourier descriptors and time warping distance. IEEE Trans. Pattern Anal. Mach. Intell. 27, 1, 142--147. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Belongie, S., Malik, J., and Puzicha, J. 2002. Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24, 4, 509--522. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Benchathlon. 2005. Benchathlon homepage. http://www.benchathlon.net.Google ScholarGoogle Scholar
  15. Berezhnoy, I. E., Postma, E. O., and Herik, J. V. D. 2005. Computerized visual analysis of paintings. In Proceedings of the International Conference on the Association for History and Computing.Google ScholarGoogle Scholar
  16. Berretti, S., Bimbo, A. D., and Vicario, E. 2001. Efficient matching and indexing of graph models in content-based retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 23, 10, 1089--1105. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Berretti, S. and Del Bimbo, A. 2006. Modeling spatial relationships between 3D objects. In Proceedings of the 18th International IEEE Conference on Pattern Recognition (ICPR). Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Berretti, S., Del Bimbo, A., and Pala, P. 2000. Retrieval by shape similarity with perceptual distance and effective indexing. IEEE Trans. Multimed. 2, 4, 225--239. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Berretti, S., Del Bimbo, A., and Vicario, E. 2003. Weighted walkthroughs between extended entities for retrieval by spatial arrangement. IEEE Trans. Multimed. 5, 1, 52--70. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Bertini, E., Cali, A., Catarci, T., Gabrielli, S., and Kimani, S. 2005. Interaction-Based adaptation for small screen devices. In Proceedings of the 10th International Conference on User Modeling (UM). Lecture Notes in Computer Science, Vol. 3538. Springer, 277--281. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Bertini, M., Cucchiara, R., Del Bimbo, A., and Prati, A. 2003. Object and event detection for semantic annotation and transcoding. In Proceedings of the IEEE International Conference on Multimedia and Expo (ICME). Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Blei, D. M. and Jordan, M. I. 2003. Modeling annotated data. In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Bohm, C., Berchtold, S., and Keim, D. A. 2001. Searching in high-dimensional space index structures for improving the performance of multimedia databases. ACM Comput. Surv. 33, 3, 322--373. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Bouchard, G. and Triggs, B. 2005. Hierarchical part-based visual object categorization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Cai, D., He, X., Li, Z., Ma, W. Y., and Wen, J. R. 2004. Hierarchical clustering of www image search results using visual, textual and link information. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Caltech101. 2004. http://www.vision.caltech.edu/image_datasets/caltech101/caltech101.html.Google ScholarGoogle Scholar
  27. Carballido-Gamio, J., Belongie, S., and Majumdar, S. 2004. Normalized cuts in 3-D for spinal MRI segmentation. IEEE Trans. Medical Imag. 23, 1, 36--44.Google ScholarGoogle ScholarCross RefCross Ref
  28. Carneiro, G. and Lowe, D. 2006. Sparse flexible models of local features. In Proceedings of the European Conference on Computer Vision (ECCV). Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Carneiro, G. and Vasconcelos, N. 2005. Minimum Bayes error features for visual recognition by sequential feature selection and extraction. In Proceedings of the Canadian Conference on Computer and Robot Vision. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Carson, C., Belongie, S., Greenspan, H., and Malik, J. 2002. Blobworld: Image segmentation using expectation-maximization and its application to image querying. IEEE Trans. Pattern Anal. Mach. Intell. 24, 8, 1026--1038. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Chalechale, A., Naghdy, G., and Mertins, A. 2005. Sketch-based image matching using angular partitioning. IEEE Trans. Syst. Man Cybernet. 35, 1, 28--41. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Chang, E. Y., Goh, K., Sychay, G., and Wu, G. 2003. CBSA: Content-based soft annotation for multimodal image retrieval using Bayes point machines. IEEE Trans. Circ. Systems Video Technol. 13, 1, 26--38. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Chang, S., Shi, Q., and Yan, C. 1987. Iconic indexing by 2-D strings. IEEE Trans. Pattern Anal. Mach. Intell. 9, 3, 413--427. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Chang, S., Yan, C., Dimitroff, D., and Arndt, T. 1988. An intelligent image database system. IEEE Trans. Softw. Eng. 14, 5, 681--688. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Chang, S.-F., Smith, J., Beigi, M., and Benitez, A. 1997. Visual information retrieval from large distributed online repositories. Commun. ACM 40, 12, 63--71. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Chen, C.-C., Wactlar, H., Wang, J. Z., and Kiernan, K. 2005. Digital imagery for significant cultural and historical materials---An emerging research field bridging people, culture, and technologies. Int. J. Digital Libr. 5, 4, 275--286.Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Chen, J., Pappas, T., Mojsilovic, A., and Rogowitz, B. 2002. Adaptive image segmentation based on color and texture. In Proceedings of the IEEE International Conference on Image Processing (ICIP).Google ScholarGoogle Scholar
  38. Chen, L. Q., Xie, X., Fan, X., Ma, W. Y., Zhang, H. J., and Zhou, H. Q. 2003. A visual attention model for adapting images on small displays. Multimed. Syst. 9, 4, 353--364.Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Chen, Y. and Wang, J. Z. 2002. A region-based fuzzy feature matching approach to content-based image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 24, 9, 252--1267. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Chen, Y. and Wang, J. Z. 2004. Image categorization by learning and reasoning with regions. J. Mach. Learn. Res. 5, 913--939. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Chen, Y., Wang, J. Z., and Krovetz, R. 2005. Clue: Cluster-based retrieval of images by unsupervised learning. IEEE Trans. Image Process. 14, 8, 1187--1201. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Chen, Y., Zhou, X., and Huang, T. S. 2002. One-class SVM for learning in image retrieval. In Proceedings of the IEEE International Conference on Image Processing (ICIP).Google ScholarGoogle Scholar
  43. Chew, M. and Tygar, J. D. 2004. Image recognition captchas. In Proceedings of the Information Security Conference.Google ScholarGoogle Scholar
  44. Choi, Y. and Rasmussen, E. M. 2002. User's relevance criteria in image retrieval in American history. Inf. Process. Manage. 38, 5, 695--726. Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Christel, M. G. and Conescu, R. M. 2005. Addressing the challenge of visual information access from digital image and video libraries. In Proceedings of the 5th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL). Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Ciaccia, P., Patella, M., and Zezula, P. 1997. M-Tree: An efficient access method for similarity search in metric spaces. In Proceedings of the International Conference on Very Large Databases (VLDB). Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. CNN. 2005. Computer decodes Mona Lisa's smile. http://www.cnn.com/2005/TECH/12/16/mona.lisa.smile/index.html.Google ScholarGoogle Scholar
  48. Comaniciu, D. and Meer, P. 2002. Mean shift: A robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24, 5, 603--619. Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. Cox, I. J., Miller, M. L., Minka, T. P., Papathomas, T. V., and Yianilos, P. N. 2000. The Bayesian image retrieval system, PicHunter: Theory, implementation, and psychophysical experiments. IEEE Trans. Image Process. 9, 1, 20--37. Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. Csillaghy, A., Hinterberger, H., and Benz, A. 2000. Content based image retrieval in astronomy. Inf. Retriev. 3, 3, 229--241. Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. Cucchiara, R., Grana., C., and Prati, A. 2003. Semantic video transcoding using classes of relevance. Int. J. Image Graph. 3, 1, 145--170.Google ScholarGoogle ScholarCross RefCross Ref
  52. Cunningham, S. J., Bainbridge, D., and Masoodian, M. 2004. How people describe their image information needs: A grounded theory analysis of visual arts queries. In Proceedings of the ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL). Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. Dagli, C. and Huang, T. S. 2004. A framework for grid-based image retrieval. In Proceedings of the International IEEE Conference on Pattern Recognition (ICPR). Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. Datta, R., Ge, W., Li, J., and Wang, J. Z. 2007. Toward bridging the annotation-retrieval gap in image search. IEEE Multimed. 14, 3, 24--35. Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. Datta, R., Joshi, D., Li, J., and Wang, J. Z. 2006. Studying aesthetics in photographic images using a computational approach. In Proceedings of the European Conference on Computer Vision (ECCV). Google ScholarGoogle ScholarDigital LibraryDigital Library
  56. Datta, R., Joshi, D., Li, J., and Wang, J. Z. 2007. Tagging over time: Real-world image annotation by lightweight meta-learning. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  57. Datta, R., Li, J., and Wang, J. Z. 2005. IMAGINATION: A robust image-based captcha generation system. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  58. Datta, R., Li, J., and Wang, J. Z. 2007. Learning the consensus on visual quality for next-generation image management. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  59. de Silva, V. and Tenenbaum, J. 2003. Global versus local methods in nonlinear dimensionality reduction. In Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS).Google ScholarGoogle Scholar
  60. Del Bimbo, A. D. 1999. Visual Information Retrieval. Morgan Kaufmann. Google ScholarGoogle ScholarDigital LibraryDigital Library
  61. Del Bimbo, A. and Pala, P. 1997. Visual image retrieval by elastic matching of user sketches. IEEE Trans. Pattern Anal. Mach. Intell. 19, 2, 121--132. Google ScholarGoogle ScholarDigital LibraryDigital Library
  62. Deng, Y. and Manjunath, B. 2001. Unsupervised segmentation of color-texture regions in images and video. IEEE Trans. Pattern Anal. Mach. Intell. 23, 8, 800--810. Google ScholarGoogle ScholarDigital LibraryDigital Library
  63. Deng, Y., Manjunath, B. S., Kenney, C., Moore, M. S., and Shin, H. 2001. An efficient color representation for image retrieval. IEEE Trans. Image Process. 10, 1, 140--147. Google ScholarGoogle ScholarDigital LibraryDigital Library
  64. Do, M. N. and Vetterli, M. 2002. Wavelet-Based texture retrieval using generalized Gaussian density and Kullback-Leibler distance. IEEE Trans. Image Process. 11, 2, 146--158. Google ScholarGoogle ScholarDigital LibraryDigital Library
  65. Dong, A. and Bhanu, B. 2003. Active concept learning for image retrieval in dynamic databases. In Proceedings of the IEEE International Conference on Computer Vision (ICCV). Google ScholarGoogle ScholarDigital LibraryDigital Library
  66. Du, Y. and Wang, J. Z. 2001. A scalable integrated region-based image retrieval system. In Proceedings of the IEEE International Conference on Image Processing (ICIP).Google ScholarGoogle Scholar
  67. Duygulu, P., Barnard, K., de Freitas, N., and Forsyth, D. 2002. Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary. In Proceedings of the European Conference on Computer Vision (ECCV). Google ScholarGoogle ScholarDigital LibraryDigital Library
  68. Fagin, R. 1997. Combining fuzzy information from multiple systems. In Proceedings of the ACM-SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS). Google ScholarGoogle ScholarDigital LibraryDigital Library
  69. Fang, Y. and Geman, D. 2005. Experiments in mental face retrieval. In Proceedings of the International Conference on Audio- and Video-Based Biometric Person Authentication (AVBPA). Google ScholarGoogle ScholarDigital LibraryDigital Library
  70. Fang, Y., Geman, D., and Boujemaa, N. 2005. An interactive system for mental face retrieval. In Proceedings of the ACM SIGMM International Workshop on Multimedia Information Retrieval (MIR) at the International Multimedia Conference. Google ScholarGoogle ScholarDigital LibraryDigital Library
  71. Feng, H., Shi, R., and Chua, T. S. 2004. A bootstrapping framework for annotating and retrieving www images. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  72. Fergus, R., Perona, P., and Zisserman, A. 2003. Object class recognition by unsupervised scale-invariant learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Google ScholarGoogle Scholar
  73. Fergus, R., Perona, P., and Zisserman, A. 2005. A sparse object category model for efficient learning and exhaustive recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Google ScholarGoogle ScholarDigital LibraryDigital Library
  74. Finlayson, G. 1996. Color in perspective. IEEE Trans. Pattern Anal. Mach. Intell. 18, 10, 1034--1038. Google ScholarGoogle ScholarDigital LibraryDigital Library
  75. Flickner, M., Sawhney, H., Niblack, W., Ashley, J., Huang, Q., Dom, B., Gorkani, M., Hafner, J., Lee, D., Petkovic, D., Steele, D., and Yanker, P. 1995. Query by image and video content: The QBIC system. IEEE Comput. 28, 9, 23--32. Google ScholarGoogle ScholarDigital LibraryDigital Library
  76. Flickr. 2002. The flickr homepage. http://www.flickr.com.Google ScholarGoogle Scholar
  77. Gao, B., Liu, T.-Y., Qin, T., Zheng, X., Cheng, Q.-S., and Ma, W.-Y. 2005. Web image clustering by consistent utilization of visual features and surrounding texts. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  78. Gevers, T. and Smeulders, A. 2000. Pictoseek: Combining color and shape invariant features for image retrieval. IEEE Trans. Image Process, 9, 1, 102--119. Google ScholarGoogle ScholarDigital LibraryDigital Library
  79. GlobalMemoryNet. 2006. Global Memory Net homepage. http://www.memorynet.org.Google ScholarGoogle Scholar
  80. Goh, K.-S., Chang, E. Y., and Cheng, K.-T. 2001. SVM binary classifier ensembles for image classification. In Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM). Google ScholarGoogle ScholarDigital LibraryDigital Library
  81. Goh, K.-S., Chang, E. Y., and Lai, W.-C. 2004. Multimodal concept-dependent active learning for image retrieval. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  82. Google Scholar. 2004. Google scholar homepage. http://scholar.google.com.Google ScholarGoogle Scholar
  83. Gordon, S., Greenspan, H., and Goldberger, J. 2003. Applying the information bottleneck principle to unsupervised clustering of discrete and continuous image representations. In Proceedings of the IEEE International Conference on Computer Vision (ICCV). Google ScholarGoogle ScholarDigital LibraryDigital Library
  84. Gouet, V. and Boujemaa, N. 2002. On the robustness of color points of interest for image retrieval. In Proceedings of the IEEE International Conference on Image Processing (ICIP).Google ScholarGoogle Scholar
  85. Grauman, K. and Darrell, T. 2005. Efficient image matching with distributions of local invariant features. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Google ScholarGoogle ScholarDigital LibraryDigital Library
  86. Gunther, N. J. and Beratta, G. B. 2001. Benchmark for image retrieval using distributed systems over the Internet: Birds-I. In Proceedings of the SPIE Conference on Internet Imaging III, Vol. 4311, 252--267.Google ScholarGoogle Scholar
  87. Gupta, A. and Jain, R. 1997. Visual information retrieval. Commun. ACM 40, 5, 70--79. Google ScholarGoogle ScholarDigital LibraryDigital Library
  88. Guyon, I. and Elisseeff, A. 2003. An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157--1182. Google ScholarGoogle ScholarDigital LibraryDigital Library
  89. Hadjidemetriou, E., Grossberg, M. D., and Nayar, S. K. 2004. Multiresolution histograms and their use for recognition. IEEE Trans. Pattern Anal. Mach. Intell. 26, 7, 831--847. Google ScholarGoogle ScholarDigital LibraryDigital Library
  90. Han, J., Ngan, K. N., Li, M., and Zhang, H.-J. 2005. A memory learning framework for effective image retrieval. IEEE Trans. Image Process. 14, 4, 511--524. Google ScholarGoogle ScholarDigital LibraryDigital Library
  91. Haralick, R. 1979. Statistical and structural approaches to texture. Proc. IEEE 67, 5, 786--804.Google ScholarGoogle ScholarCross RefCross Ref
  92. Hastie, T., Tibshirani, R., and Friedman, J. 2001. The Elements of Statistical Learning. Springer-Verlag.Google ScholarGoogle Scholar
  93. Hauptmann, A. G. and Christel, M. G. 2004. Successful approaches in the TREC video retrieval evaluations. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  94. He, J., Li, M., Zhang, H.-J., Tong, H., and Zhang, C. 2004a. Manifold-ranking based image retrieval. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  95. He, J., Li, M., Zhang, H.-J., Tong, H., and Zhang, C. 2004b. Mean version space: A new active learning method for content-based image retrieval. In Proceedings of the ACM SIGMM International Workshop on Multimedia Information Retrieval (MIR) at the International Multimedia Conference. Google ScholarGoogle ScholarDigital LibraryDigital Library
  96. He, X. 2004. Incremental semi-supervised subspace learning for image retrieval. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  97. He, X., Ma, W.-Y., and Zhang, H.-J. 2004c. Learning an image manifold for retrieval. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  98. Hoi, C.-H. and Lyu, M. R. 2004a. Group-based relevance feedback with support vector machine ensembles. In Proceedings of the IEEE International Conference on Pattern Recognition (ICPR). Google ScholarGoogle ScholarDigital LibraryDigital Library
  99. Hoi, C.-H. and Lyu, M. R. 2004b. A novel log-based relevance feedback technique in content-based image retrieval. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  100. Hoiem, D., Sukthankar, R., Schneiderman, H., and Huston, L. 2004. Object-based image retrieval using the statistical structure of images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Google ScholarGoogle ScholarDigital LibraryDigital Library
  101. Huang, J., Ravi Kumar, S., Mitra, M., Zhu, W.-J., and Zabih, R. 1999. Spatial color indexing and applications. Int. J. Comput. Vision 35, 3, 245--268. Google ScholarGoogle ScholarDigital LibraryDigital Library
  102. Huijsmans, D. P. and Sebe, N. 2005. How to complete performance graphs in content-based image retrieval: Add generality and normalize scope. IEEE Trans. Pattern Anal. Mach. Intell. 27, 2, 245--251. Google ScholarGoogle ScholarDigital LibraryDigital Library
  103. Huynh, D. F., Drucker, S. M., Baudisch, P., and Wong, C. 2005. Time quilt: Scaling up zoomable photo browsers for large, unstructured photo collections. In Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI). Google ScholarGoogle ScholarDigital LibraryDigital Library
  104. IEEE(TIP). 2004. Special issue on image processing for cultural heritage. IEEE Trans. Image Process. 13, 3. Google ScholarGoogle ScholarDigital LibraryDigital Library
  105. ImageCLEF. 2006. The CLEF cross language image retrieval track (ImageCLEF) homepage. http://ir.shef.ac.uk/imageclef.Google ScholarGoogle Scholar
  106. ImagEVAL. 2005. The ImageEval homepage. http://www.imageval.org.Google ScholarGoogle Scholar
  107. Iqbal, Q. and Aggarwal, J. K. 2002. Retrieval by classification of images containing large manmade objects using perceptual grouping. Pattern Recogn. J. 35, 7, 1463--1479.Google ScholarGoogle ScholarCross RefCross Ref
  108. Jaimes, A., Omura, K., Nagamine, T., and Hirata, K. 2004. Memory cues for meeting video retrieval. In Proceedings of the 1st ACM Workshop on Continuous Archival and Retrieval of Personal Experiences (CARPE) at the ACM International Multimedia Conference. Google ScholarGoogle ScholarDigital LibraryDigital Library
  109. Jaimes, A., Sebe, N., and Gatica-Perez, D. 2006. Human-centered computing: A multimedia perspective. In Proceedings of the ACM International Conference on Multimedia (Special Session on Human-Centered Multimedia). Google ScholarGoogle ScholarDigital LibraryDigital Library
  110. Jain, A. and Farrokhnia, F. 1990. Unsupervised texture segmentation using Gabor filters. Proceedings of the International Conference on Systems, Man and Cybernetics.Google ScholarGoogle Scholar
  111. Jansen, B. J., Spink, A., and Pedersen, J. 2003. An analysis of multimedia searching on AltaVista. In Proceedings of the ACM SIGMM International Workshop on Multimedia Information Retrieval (MIR) at the International Multimedia Conference. Google ScholarGoogle ScholarDigital LibraryDigital Library
  112. Jeon, J., Lavrenko, V., and Manmatha, R. 2003. Automatic image annotation and retrieval using cross-media relevance models. In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Google ScholarGoogle ScholarDigital LibraryDigital Library
  113. Jeong, S., Won, C. S., and Gray, R. 2004. Image retrieval using color histograms generated by Gauss mixture vector quantization. Comput. Vision Image Understand. 9, 1--3, 44--66. Google ScholarGoogle ScholarDigital LibraryDigital Library
  114. Johnson Jr., C. R., Hendriks, E., Berezhony, I., Brevdo, E., Hughes, S., Daubechies, I., Li, J., Postma, E., and Wang, J. Z. 2008. Image processing for artist identification---computerized analysis of Vincent van Gogh's painting brush stokes. IEEE Sign. Process. 25 (Special Issue on Visual Cultural Heritage).Google ScholarGoogle Scholar
  115. Jin, R., Chai, J. Y., and Si, L. 2004. Effective automatic image annotation via a coherent language model and active learning. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  116. Jin, R. and Hauptmann, A. 2002. Using a probabilistic source model for comparing images. In Proceedings of the IEEE International Conference on Image Processing (ICIP).Google ScholarGoogle Scholar
  117. Jin, Y., Khan, L., Wang, L., and Awad, M. 2005. Image annotations by combining multiple evidence and Wordnet. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  118. Jing, F., Li, M., Zhang, H.-J., and Zhang, B. 2004a. An efficient and effective region-based image retrieval framework. IEEE Trans. Image Process. 13, 5, 699--709. Google ScholarGoogle ScholarDigital LibraryDigital Library
  119. Jing, F., Li, M., Zhang, H.-J., and Zhang, B. 2004b. Relevance feedback in region-based image retrieval. IEEE Trans. Circ. Syst. Video Technol. 14, 5, 672--681. Google ScholarGoogle ScholarDigital LibraryDigital Library
  120. Jing, F., Li, M., Zhang, H. J., and Zhang, B. 2005. A unified framework for image retrieval using keyword and visual features. IEEE Trans. Image Process. 14, 7, 978--989. Google ScholarGoogle ScholarDigital LibraryDigital Library
  121. Jing, F., Wang, C., Yao, Y., Deng, K., Zhang, L., and Ma, W. Y. 2006. Igroup: Web image search results clustering. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  122. Joshi, D., Datta, R., Zhuang, Z., Weiss, W., Friedenberg, M., Wang, J., and Li, J. 2006a. Paragrab: A comprehensive architecture for Web image management and multimodal querying. In Proceedings of the International Conference on VLDB. Google ScholarGoogle ScholarDigital LibraryDigital Library
  123. Joshi, D., Naphade, M., and Natsev, A. 2007. A greedy performance driven algorithm for decision fusion learning. In Proceedings of the IEEE International Conference on Image Processing (ICIP).Google ScholarGoogle Scholar
  124. Joshi, D., Wang, J. Z., and Li, J. 2006b. The story picturing engine---a system for automatic text illustration. ACM Trans. Multimed. Comput. Commun. Appl. 2, 1, 68--89. Google ScholarGoogle ScholarDigital LibraryDigital Library
  125. Kaster, T., Pfeiffer, M., and Bauckhage, C. 2003. Combining speech and haptics for intuitive and efficient navigation through image databases. In Proceedings of the 5th International Conference on Multimidia Interfaces (ICMI). Google ScholarGoogle ScholarDigital LibraryDigital Library
  126. Ke, Y., Sukthankar, R., and Huston, L. 2004. Efficient near-duplicate detection and subimage retrieval. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  127. Kherfi, M. L., Ziou, D., and Bernardi, A. 2004. Image retrieval from the World Wide Web: Issues, techniques, and systems. ACM Comput. Surv. 36, 1, 35--67. Google ScholarGoogle ScholarDigital LibraryDigital Library
  128. Kim, D.-H. and Chung, C.-W. 2003. Qcluster: Relevance feedback using adaptive clustering for content based image retrieval. In Proceedings of the ACM International Conference on Management of Data (SIGMOD). Google ScholarGoogle ScholarDigital LibraryDigital Library
  129. Kim, Y. S., Street, W. N., and Menczer, F. 2000. Feature selection in unsupervised learning via evolutionary search. In Proceedings of the ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD). Google ScholarGoogle ScholarDigital LibraryDigital Library
  130. Ko, B. and Byun, H. 2002. Integrated region-based image retrieval using region's spatial relationships. In Proceedings of the IEEE International Conference on Pattern Recognition (ICPR). Google ScholarGoogle ScholarDigital LibraryDigital Library
  131. Kotoulas, L. and Andreadis, I. 2003. Colour histogram content-based image retrieval and hardware implementation. IEEE Proc. Circ. Dev. Syst. 150, 5, 387--393.Google ScholarGoogle ScholarCross RefCross Ref
  132. Laaksonen, J., Koskela, M., Laakso, S., and Oja, E. 2001. Self-organizing maps as a relevance feedback technique in content-based image retrieval. Pattern Anal. Appl. 4, 140--152.Google ScholarGoogle ScholarCross RefCross Ref
  133. Laaksonen, J., Koskela, M., and Oja, E. 2002. PicSom-Self-Organizing image retrieval with mpeg-7 content descriptors. IEEE Trans. Neural Netw. 13, 4, 841--853. Google ScholarGoogle ScholarDigital LibraryDigital Library
  134. Latecki, L. J. and Lakamper, R. 2000. Shape similarity measure based on correspondence of visual parts. IEEE Trans. Pattern Anal. Mach. Intell. 22, 10, 1185--1190. Google ScholarGoogle ScholarDigital LibraryDigital Library
  135. Lavrenko, V., Manmatha, R., and Jeon, J. 2003. A model for learning the semantics of pictures. In Proceedings of the Conference on Advances in Neural Information Pracessing Systems (NIPS).Google ScholarGoogle Scholar
  136. Lazebnik, S., Schmid, C., and Ponce, J. 2003. Affine-invariant local descriptors and neighborhood statistics for texture recognition. In Proceedings of the IEEE International Conference on Computer Vision (ICCV). Google ScholarGoogle ScholarDigital LibraryDigital Library
  137. Levina, E. and Bickel, P. 2001. The earth mover's distance is the Mallows distance: Some insights from statistics. In Proceedings of the IEEE International Conference on Computer Vision (ICCV).Google ScholarGoogle Scholar
  138. Lew, M., Sebe, N., Djeraba, C., and Jain, R. 2006. Content-based multimedia information retrieval: State-of-the-art and challenges. ACM Trans. Multimed. Comput. Commun. Appl. 2, 1, 1--19. Google ScholarGoogle ScholarDigital LibraryDigital Library
  139. Li, B., Goh, K.-S., and Chang, E. Y. 2003. Confidence-based dynamic ensemble for image annotation and semantics discovery. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  140. Li, J. 2005. Two-scale image retrieval with significant meta-information feedback. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  141. Li, J., Gray, R. M., and Olshen, R. A. 2000. Multiresolution image classification by hierarchical modeling with two dimensional hidden Markov models. IEEE Trans. Inf. Theory 46, 5, 1826--1841. Google ScholarGoogle ScholarDigital LibraryDigital Library
  142. Li, J., Najmi, A., and Gray, R. M. 2000. Image classification by a two dimensional hidden Markov model. IEEE Trans. Signal Process. 48, 2, 527--533. Google ScholarGoogle ScholarDigital LibraryDigital Library
  143. Li, J. and Sun, H.-H. 2003. On interactive browsing of large images. IEEE Trans. Multimed. 5, 4, 581--590. Google ScholarGoogle ScholarDigital LibraryDigital Library
  144. Li, J. and Wang, J. Z. 2003. Automatic linguistic indexing of pictures by a statistical modeling approach. IEEE Trans. Pattern Anal. Mach. Intell. 25, 9, 1075--1088. Google ScholarGoogle ScholarDigital LibraryDigital Library
  145. Li, J. and Wang, J. Z. 2004. Studying digital imagery of ancient paintings by mixtures of stochastic models. IEEE Trans. Image Process. 13, 3, 340--353. Google ScholarGoogle ScholarDigital LibraryDigital Library
  146. Li, J. and Wang, J. Z. 2006. Real-Time computerized annotation of pictures. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  147. Li, J. and Wang, J. Z. 2008. Real-time computerized annotation of pictures. IEEE Trans. Pattern Anal. Mach. Intell. 30, 6, 985--1002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  148. Li, J., Wang, J. Z., and Wiederhold, G. 2000. IRM: Integrated region matching for image retrieval. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  149. Li, Z.-W., Xie, X., Liu, H., Tang, X., Li, M., and Ma, W.-Y. 2004. Intuitive and effective interfaces for www image search engines. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  150. Lin, Y.-Y., Liu, T.-L., and Chen, H.-T. 2005. Semantic manifold learning for image retrieval. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  151. Liu, W. and Tang, X. 2005. Learning an image-word embedding for image auto-annotation on the nonlinear latent space. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  152. Lu, Y., Hu, C., Zhu, X., Zhang, H., and Yang, Q. 2000. A unified framework for semantics and feature based relevance feedback in image retrieval systems. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  153. Lyu, S., Rockmore, D., and Farid, H. 2004. A digital technique for art authentication. Proc. Nat. Acad. Sci. 101, 49, 17006--17010.Google ScholarGoogle ScholarCross RefCross Ref
  154. Ma, W. and Manjunath, B. 1997. Netra: A toolbox for navigating large image databases. In Proceedings of the IEEE International Conference on Image Processing (ICIP). Google ScholarGoogle ScholarDigital LibraryDigital Library
  155. Ma, W.-Y. and Manjunath, B. 1998. Texture thesaurus for browsing large aerial photographs. J. Amer. Soc. Inf. Sci. 49, 7, 633--648. Google ScholarGoogle ScholarDigital LibraryDigital Library
  156. Maitre, H., Schmitt, F., and Lahanier, C. 2001. 15 years of image processing and the fine arts. In Proceedings of the IEEE International Conference on Image Processing (ICIP).Google ScholarGoogle Scholar
  157. Malik, J., Belongie, S., Leung, T. K., and Shi, J. 2001. Contour and texture analysis for image segmentation. Int. J. Comput. Vision 43, 1, 7--27. Google ScholarGoogle ScholarDigital LibraryDigital Library
  158. Malik, J. and Perona, P. 1990. Preattentive texture discrimination with early vision mechanisms. J. Optical Soc. Amer. A 7, 5, 923--932.Google ScholarGoogle ScholarCross RefCross Ref
  159. Mallows, C. L. 1972. A note on asymptotic joint normality. Ann. Math. Statist. 43, 2, 508--515.Google ScholarGoogle ScholarCross RefCross Ref
  160. Manjunath, B. and Ma, W.-Y. 1996. Texture features for browsing and retrieval of image data. IEEE Trans. Pattern Anal. Mach. Intell. 18, 8, 837--842. Google ScholarGoogle ScholarDigital LibraryDigital Library
  161. Manjunath, B. S., Ohm, J.-R., Vasudevan, V. V., and Yamada, A. 2001. Color and texture descriptors. IEEE Trans. Circ. Syst. Video Technol. 11, 6, 703--715. Google ScholarGoogle ScholarDigital LibraryDigital Library
  162. Marsicoi, M. D., Cinque, L., and Levialdi, S. 1997. Indexing pictorial documents by their content: A survey of current techniques. Image Vision Comput. 15, 2, 119--141. Google ScholarGoogle ScholarDigital LibraryDigital Library
  163. Martinez, K., Cupitt, J., Saunders, D., and Pillay, R. 2002. Ten years of art imaging research. Proc. IEEE 90, 28--41.Google ScholarGoogle ScholarCross RefCross Ref
  164. Mathiassen, J. R., Skavhaug, A., and Bo, K. 2002. Texture similarity measure using Kullback-Leibler divergence between gamma distributions. In Proceedings of the European Conference on Computer Vision (ECCV). Google ScholarGoogle ScholarDigital LibraryDigital Library
  165. McLachlan, G. and Peel, D. 2000. Finite Mixture Models. Wiley-Interscience.Google ScholarGoogle Scholar
  166. Mehrotra, R. and Gary, J. E. 1995. Similar-Shape retrieval in shape data management. IEEE Comput. 28, 9, 57--62. Google ScholarGoogle ScholarDigital LibraryDigital Library
  167. Melzer, T., Kammerer, P., and Zolda, E. 1998. Stroke detection of brush strokes in protrait miniatures using a semi-parametric and a model based approach. In Proceedings of the International IEEE Conference on Pattern Recognition (ICPR). Google ScholarGoogle ScholarDigital LibraryDigital Library
  168. Mikolajczk, K. and Schmid, C. 2003. A performance evaluation of local descriptors. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Google ScholarGoogle Scholar
  169. Mikolajczyk, K. and Schmid, C. 2004. Scale and affine invariant interest point detectors. Intl. J. Comput. Vision 60, 1, 63--86. Google ScholarGoogle ScholarDigital LibraryDigital Library
  170. Miller, G. 1995. Wordnet: A lexical database for English. Comm. ACM 38, 11, 39--41. Google ScholarGoogle ScholarDigital LibraryDigital Library
  171. Mirsky, S.. 2006. Computers get the picture. Sci. Amer. (Nov.).Google ScholarGoogle Scholar
  172. Mitra, P., Murthy, C., and Pal, S. 2002. Unsupervised feature selection using feature similarity. IEEE Trans. Pattern Anal. Mach. Intell. 24, 3, 301--312. Google ScholarGoogle ScholarDigital LibraryDigital Library
  173. Mokhtarian, F. 1995. Silhouette-Based isolated object recognition through curvature scale space. IEEE Trans. Pattern Anal. Mach. Intell. 17, 5, 539--544. Google ScholarGoogle ScholarDigital LibraryDigital Library
  174. Monay, F. and Gatica-Perez, D. 2003. On image auto-annotation with latent space models. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  175. Mori, G. and Malik, J. 2003. Recognizing objects in adversarial clustter: Breaking a visual captcha. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Google ScholarGoogle ScholarDigital LibraryDigital Library
  176. Mukherjea, S., Hirata, K., and Hara, Y. 1999. Amore: A World Wide Web image retrieval engine. In Proceedings of the International World Wide Web Conference (WWW).Google ScholarGoogle Scholar
  177. Muller, H., Marchand-Maillet, S., and Pun, T. 2002. The truth about Corel---Evaluation in image retrieval. In Proceedings of the International Conference on Video Retrieval (CIVR). Lecture Notes in Computer Science, vol. 2383. Springer, 36--45. Google ScholarGoogle ScholarDigital LibraryDigital Library
  178. Muller, H., Michoux, N., Bandon, D., and Geissbuhler, A. 2004. A review of content-based image retrieval systems in medical applications---Clinical benefits and future directions. Int. J. Medical Inf. 73, 1, 1--23.Google ScholarGoogle ScholarCross RefCross Ref
  179. Muller, H., Muller, W., Squire, D. M., Marchand-Maillet, S., and Pun, T. 2001. Performance evaluation in content-based image retrieval: Overview and proposals. Pattern Recogn. Lett. 22, 5, 593--601. Google ScholarGoogle ScholarDigital LibraryDigital Library
  180. Muller, H., Pun, T., and Squire, D. 2004. Learning from user behavior in image retrieval: Application of market basket analysis. Int. J. Comput. Vision 56, 1--2, 65--77. Google ScholarGoogle ScholarDigital LibraryDigital Library
  181. Nagamine, T., Jaimes, A., Omura, K., and Hirata, K. 2004. A visuospatial memory cue system for meeting video retrieval. In Proceedings of the ACM International Conference on Multimedia (demonstration). Google ScholarGoogle ScholarDigital LibraryDigital Library
  182. Nakano, K. and Takamichi, E. 2003. An image retrieval system using fpgas. In Proceedings of the Asia South Pacific Design Automation Conference. Google ScholarGoogle ScholarDigital LibraryDigital Library
  183. Nakazato, M., Dagli, C., and Huang, T. 2003. Evaluating group-based relevance feedback for content-based image retrieval. In Proceedings of the IEEE International Conference on Image Processing (ICIP).Google ScholarGoogle Scholar
  184. Natsev, A., Naphade, M. R., and Tesic, J. 2005. Learning the semantics of multimedia queries and concepts from a small number of examples. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  185. Natsev, A., Rastogi, R., and Shim, K. 2004. Walrus: A similarity retrieval algorithm for image databases. IEEE Trans. Knowl. Data Eng. 16, 3, 301--316. Google ScholarGoogle ScholarDigital LibraryDigital Library
  186. Natsev, A. and Smith, J. 2002. A study of image retrieval by anchoring. In Proceedings of the IEEE International Conference on Multimedia and Expo (ICME).Google ScholarGoogle Scholar
  187. Ng, T.-T., Chang, S.-F., Hsu, J., Xie, L., and Tsui, M.-P. 2005. Physics-Motivated features for distinguishing photographic images and computer graphics. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  188. Painter, T. H., Dozier, J., Roberts, D. A., Davis, R. E., and Green, R. O. 2003. Retrieval of subpixel snow-covered area and grain size from imaging spectrometer data. Remote Sens. Env. 85, 1, 64--77.Google ScholarGoogle ScholarCross RefCross Ref
  189. Panda, N. and Chang, E. Y. 2006. Efficient top-k hyperplane query processing for multimedia information retrieval. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  190. Pentland, A., Picard, R., and Sclaroff, S. 1994. Photobook: Tools for content-based manipulation of image databases. In Proceedings of the Conference on Storage and Retrieval for Image and Video Database II, SPIE, San Jose, CA.Google ScholarGoogle Scholar
  191. Petraglia, G., Sebillo, M., Tucci, M., and Tortora, G. 2001. Virtual images for similarity retrieval in image databases. IEEE Trans. Knowl. Data Eng. 13, 6, 951--967. Google ScholarGoogle ScholarDigital LibraryDigital Library
  192. Petrakis, E. and Faloutsos, A. 1997. Similarity searching in medical image databases. IEEE Trans. Knowl. Data Eng. 9, 3, 435--447. Google ScholarGoogle ScholarDigital LibraryDigital Library
  193. Petrakis, E. G. M., Diplaros, A., and Milios, E. 2002. Matching and retrieval of distorted and occluded shapes using dynamic programming. IEEE Trans. Pattern Anal. Mach. Intell. 24, 4, 509--522. Google ScholarGoogle ScholarDigital LibraryDigital Library
  194. Petrakis, E. G. M., Faloutsos, C., and Lin, K. I. 2002. Imagemap: An image indexing method based on spatial similarity. IEEE Trans. Knowl. Data Eng. 14, 5, 979--987. Google ScholarGoogle ScholarDigital LibraryDigital Library
  195. Photo.Net. 1993. Photonet homepage. http://www.photo.net.Google ScholarGoogle Scholar
  196. Photo.Net(RatingSystem). Photonet standards. 1993. http://www.photo.net/gallery/photocritique/standards.Google ScholarGoogle Scholar
  197. Pi, M., Mandal, M. K., and Basu, A. 2005. Image retrieval based on histogram of fractal parameters. IEEE Trans. Multimed. 7, 4, 597--605. Google ScholarGoogle ScholarDigital LibraryDigital Library
  198. Picasa. 2004. Picasa homepage. http://picasa.google.com/.Google ScholarGoogle Scholar
  199. Portilla, J. and Simoncelli, E. 2000. A parametric texture model based on joint statistics of complex wavelet coefficients. Int. J. Comput. Vision 40, 1, 49--71. Google ScholarGoogle ScholarDigital LibraryDigital Library
  200. Quack, T., Monich, U., Thiele, L., and Manjunath, B. S. 2004. Cortina: A system for largescale, content-based Web image retrieval. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  201. Rodden, K., Basalaj, W., Sinclair, D., and Wood, K. 2001. Does organization by similarity assist image browsing? In Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI). Google ScholarGoogle ScholarDigital LibraryDigital Library
  202. Rodden, K. and Wood, K. 2003. How do people manage their digital photographs? In Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI). Google ScholarGoogle ScholarDigital LibraryDigital Library
  203. Rowe, N. C. 2002. Marie-4: A high-recall, self-improving Web crawler that finds images using captions. IEEE Intell. Syst. 17, 4, 8--14. Google ScholarGoogle ScholarDigital LibraryDigital Library
  204. Rubner, Y., Tomasi, C., and Guibas, L. J. 2000. The earth mover's distance as a metric for image retrieval. Int. J. Comput. Vision 40, 99--121. Google ScholarGoogle ScholarDigital LibraryDigital Library
  205. Rubner, Y., Tomasi, C., and Guibas, L. J. 1999. A metric for distribution with applications to image databases. In Proceedings of the IEEE International Conference on Computer Vision (ICCV). Google ScholarGoogle ScholarDigital LibraryDigital Library
  206. Rui, Y., Huang, T., and Chang, S.-F. 1999. Image retrieval: Current techniques, promising directions and open issues. J. Visual Commun. Image Represent. 10, 1, 39--62.Google ScholarGoogle ScholarDigital LibraryDigital Library
  207. Rui, Y., Huang, T., and Mehrotra, S. 1997. Content-based image retrieval with relevance feedback in Mars. In Proceedings of the IEEE International Conference on Image Processing (ICIP).Google ScholarGoogle Scholar
  208. Rui, Y. and Huang, T. S. 2000. Optimizing learning in image retrieval. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Google ScholarGoogle Scholar
  209. Rui, Y., Huang, T. S., Ortega, M., and Mehrotra, S. 1998. Relevance feedback: A power tool in interactive content-based image retrieval. IEEE Trans. Circ. Syst. Video Technol. 8, 5, 644--655. Google ScholarGoogle ScholarDigital LibraryDigital Library
  210. Sablatnig, R., Kammerer, P., and Zolda, E. 1998. Hierarchical classification of paintings using face- and brush stroke models. In Proceedings of the International IEEE Conference on Pattern Recognition (ICPR). Google ScholarGoogle ScholarDigital LibraryDigital Library
  211. Saux, B. L. and Boujemaa, N. 2002. Unsupervised robust clustering for image database categorization. In Proceedings of the International IEEE Conference on Pattern Recognition (ICPR). Google ScholarGoogle ScholarDigital LibraryDigital Library
  212. Schaffalitzky, F. and Zisserman, A. 2001. Viewpoint invariant texture matching andwide baseline stereo. In Proceedings of the IEEE International Conference on Computer Vision (ICCV).Google ScholarGoogle Scholar
  213. Schmid, C. and Mohr, R. 1997. Local grayvalue invariants for image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 19, 5, 530--535. Google ScholarGoogle ScholarDigital LibraryDigital Library
  214. Schroder, M., Rehrauer, H., Seidel, K., and Datcu, M. 2000. Interactive learning and probabilistic retrieval in remote sensing image archives. IEEE Trans. Geosci. Remote Sens. 38, 5, 2288--2298.Google ScholarGoogle ScholarCross RefCross Ref
  215. Sebe, N., Lew, M. S., and Huijsmans, D. P. 2000. Toward improved ranking metrics. IEEE Trans. Pattern Anal. Mach. Intell. 22, 10, 1132--1141. Google ScholarGoogle ScholarDigital LibraryDigital Library
  216. Sebe, N., Lew, M. S., Zhou, X., Huang, T. S., and Bakker, E. 2003. The state of the art in image and video retrieval. In Proceedings of the International Conference on Video Retrieval (CIVR). Google ScholarGoogle ScholarDigital LibraryDigital Library
  217. Shanableh, T. and Ghanbari, M. 2000. Heterogeneous video transcoding to lower spatio-temporal resolutions and different encoding formats. IEEE Trans. Multimed. 2, 2, 101--110. Google ScholarGoogle ScholarDigital LibraryDigital Library
  218. Shi, J. and Malik, J. 2000. Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22, 8, 888--905. Google ScholarGoogle ScholarDigital LibraryDigital Library
  219. Shirahatti, N. V. and Barnard, K. 2005. Evaluating image retrieval. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). Google ScholarGoogle ScholarDigital LibraryDigital Library
  220. Slashdot. 2005. Searching by image instead of keywords. Slashdot News (May).Google ScholarGoogle Scholar
  221. Smeaton, A. F. and Over, P. 2003. Benchmarking the effectiveness of information retrieval tasks on digital video. In Proceedings of the International Conference on Video Retrieval (CIVR). Google ScholarGoogle ScholarDigital LibraryDigital Library
  222. Smeulders, A. W., Worring, M., Santini, S., Gupta, A., and Jain, R. 2000. Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Mach. Intell. 22, 12, 1349--1380. Google ScholarGoogle ScholarDigital LibraryDigital Library
  223. Smith, J. and Chang, S.-F. 1997a. Integrated spatial and feature image query. IEEE Trans. Knowl. Data Eng. 9, 3, 435--447.Google ScholarGoogle Scholar
  224. Smith, J. and Chang, S.-F. 1997b. Visualseek: A fully automated content-based image query system. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  225. Smolka, B., Szczepanski, M., Lukac, R., and Venetsanopoulos, A. N. 2004. Robust color image retrieval for the World Wide Web. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP).Google ScholarGoogle Scholar
  226. Snoek, C. G. M. and Worring, M. 2005. Multimodal video indexing: A review of the state-of-the-art. Multimed. Tools Appl. 25, 1, 5--35. Google ScholarGoogle ScholarDigital LibraryDigital Library
  227. Staedter, T. 2006. Digital pics ‘read’ by computer. http://dsc.discovery.com/news/2006/11/09/images_tec. html.Google ScholarGoogle Scholar
  228. Su, Z., Zhang, H.-J., Li, S., and Ma, S. 2003. Relevance feedback in content-based image retrieval: Bayesian framework, feature subspaces, and progressive learning. IEEE Trans. Image Process. 12, 8, 924--937. Google ScholarGoogle ScholarDigital LibraryDigital Library
  229. Swain, M. and Ballard, B. 1991. Color indexing. Int. J. Comput. Vision 7, 1, 11--32. Google ScholarGoogle ScholarDigital LibraryDigital Library
  230. Swets, D. and Weng, J. 1996. Using discriminant eigenfeatures for image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 18, 8, 831--836. Google ScholarGoogle ScholarDigital LibraryDigital Library
  231. Terragalleria. 2001. Terragalleria homepage. http://www.terragalleria.com.Google ScholarGoogle Scholar
  232. Theoharatos, C., Laskaris, N. A., Economou, G., and Fotopoulos, S. 2005. A generic scheme for color image retrieval based on the multivariate Wald-Wolfowitz test. IEEE Trans. Knowl. Data Eng. 17, 6, 808--819. Google ScholarGoogle ScholarDigital LibraryDigital Library
  233. Tian, Q., Sebe, N., Lew, M. S., Loupias, E., and Huang, T. S. 2001. Image retrieval using wavelet-based salient points. J. Electron. Imag. 10, 4, 835--849.Google ScholarGoogle ScholarCross RefCross Ref
  234. Tieu, K. and Viola, P. 2004. Boosting image retrieval. Int. J. Comput. Vision 56, 1--2, 17--36. Google ScholarGoogle ScholarDigital LibraryDigital Library
  235. Tishby, N., Pereira, F., and Bialek, W. 1999. The information botflencek method. In Proceedings of the Allerton Conference on Communication and Computation.Google ScholarGoogle Scholar
  236. Tong, S. and Chang, E. 2001. Support vector machine active learning for image retrieval. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  237. Tope, A. S. and Enser, P. G. P. 2000. Design and implementation factors in electronic image retrieval systems. Res. Rep. 105, Library and Information Commission.Google ScholarGoogle Scholar
  238. Torres, R. S., Silva, C. G., Medeiros, C. B., and Rocha, H. V. 2003. Visual structures for image browsing. In Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM). Google ScholarGoogle ScholarDigital LibraryDigital Library
  239. TRECVID. 2001. TRECVID homepage. http://www-nlpir.nist.gov/projects/trecvid.Google ScholarGoogle Scholar
  240. Tu, Z. and Zhu, S.-C. 2002. Image segmentation by data-driven Markov chain Monte Carlo. IEEE Trans. Pattern Anal. Mach. Intell. 24, 5, 657--673. Google ScholarGoogle ScholarDigital LibraryDigital Library
  241. Tuytelaars, T. and van Gool, L. 1999. Content-Based image retrieval based on local affinely invariant regions. In Proceedings of the International Conference on Visual Information Systems (VISUAL). Google ScholarGoogle ScholarDigital LibraryDigital Library
  242. Unser, M. 1995. Texture classification and segmentation using wavelet frames. IEEE Trans. Image Process. 4, 11, 1549--1560. Google ScholarGoogle ScholarDigital LibraryDigital Library
  243. Vailaya, A., Figueiredo, M. A. T., Jain, A. K., and Zhang, H.-J. 2001. Image classification for content-based indexing. IEEE Trans. Image Process. 10, 1, 117--130. Google ScholarGoogle ScholarDigital LibraryDigital Library
  244. Vasconcelos, N. 2004. On the efficient evaluation of probabilistic similarity functions for image retrieval. IEEE Trans. Inf. Theory 50, 7, 1482--1496. Google ScholarGoogle ScholarDigital LibraryDigital Library
  245. Vasconcelos, N. and Lippman, A. 2000a. Learning from user feedback in image retrieval systems. In Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS).Google ScholarGoogle Scholar
  246. Vasconcelos, N. and Lippman, A. 2000b. A probabilistic architecture for content-based image retrieval. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Google ScholarGoogle Scholar
  247. Vasconcelos, N. and Lippman, A. 2005. A multiresolution manifold distance for invariant image similarity. IEEE Trans. Multimed. 7, 1, 127--142. Google ScholarGoogle ScholarDigital LibraryDigital Library
  248. Velivelli, A., Ngo, C.-W., and Huang, T. S. 2004. Detection of documentary scene changes by audio-visual fusion. In Proceedings of the International Conference on Video Retrieval (CIVR). Google ScholarGoogle ScholarDigital LibraryDigital Library
  249. Vetro, A., Christopoulos, C., and Sun, H. 2003. Video transcoding architectures and techniques: An overview. IEEE Signal Process. Mag. 20, 2, 18--29.Google ScholarGoogle ScholarCross RefCross Ref
  250. Vinay, V., Cox, I. J., Milic-Frayling, N., and Wood, K. 2004. Evaluating relevance feedback and display strategies for searching on small displays. In Proceedings of the 7th International Symposium on String Processing Information Retrieval. Lecture Notes in Computer Science, vol. 3246, 131--133.Google ScholarGoogle ScholarCross RefCross Ref
  251. Vinay, V., Cox, I. J., Milic-Frayling, N., and Wood, K. 2005. Evaluating relevance feedback algorithms for searching on small displays. In Proceedings of the European Conference on IR Research (ECIR). Lecture Notes in Computer Science, vol. 3408. Springer, 185--199. Google ScholarGoogle ScholarDigital LibraryDigital Library
  252. von Ahn, L. and Dabbish, L. 2004. Labeling images with a computer game. In Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI). Google ScholarGoogle ScholarDigital LibraryDigital Library
  253. Wang, J., Li, J., and Wiederhold, G. 2001. SIMPLIcity: Semantics-sensitive integrated matching for picture libraries. IEEE Trans. Pattern Anal. Mach. Intell. 23, 9, 947--963. Google ScholarGoogle ScholarDigital LibraryDigital Library
  254. Wang, J., Wiederhold, G., Firschein, O., and Wei, S. 1998. Content-Based image indexing and searching using Daubechies' wavelets. Int. J. Digital Librar. 1, 4, 311--328.Google ScholarGoogle ScholarCross RefCross Ref
  255. Wang, J. Z., Boujemaa, N., Del Bimbo, A., Geman, D., Hauptmann, A., and Tesic, J. 2006. Diversity in multimedia information retrieval research. In Proceedings of the ACM SIGMM International Workshop on Multimedia Information Retrieval (MIR) at the International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  256. Wang, J. Z., Li, J., Gray, R. M., and Wiederhold, G. 2001. Unsupervised multiresolution segmentation for images with low depth of field. IEEE Trans. Pattern Anal. Mach. Intell. 23, 1, 85--90. Google ScholarGoogle ScholarDigital LibraryDigital Library
  257. Wang, X.-J., Ma, W.-Y., He, Q.-C., and Li, X. 2004a. Grouping Web image search result. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  258. Wang, X. J., Ma, W. Y., Xue, G. R., and Li, X. 2004b. Multi-Model similarity propagation and its application for Web image retrieval. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  259. Wang, Y. H. 2003. Image indexing and similarity retrieval based on spatial relationship model. Inf. Sci. Inf. Comput. Sci. 154, 1-2, 39--58. Google ScholarGoogle ScholarDigital LibraryDigital Library
  260. Wang, Z., Chi, Z., and Feng, D. 2002. Fuzzy integral for leaf image retrieval. In Proceedings of the IEEE International Conference on Fuzzy Systems.Google ScholarGoogle Scholar
  261. Webe, M., Welling, M., and Perona, P. 2000. Unsupervised learning of models for recognition. In Proceedings of the European Conference on Computer Vision (ECCV). Google ScholarGoogle ScholarDigital LibraryDigital Library
  262. Weston, J., Mukherjee, S., Chapelle, O., Pontil, M., Poggio, T., and Vapnik, V. 2000. Feature selection for SVMS. In Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS).Google ScholarGoogle Scholar
  263. Wilson, R. and Hancock, E. 1997. Structural matching by discrete relaxation. IEEE Trans. Pattern Anal. Mach. Intell. 19, 6, 634--648. Google ScholarGoogle ScholarDigital LibraryDigital Library
  264. Wolfson, H. and Rigoutsos, I. 1997. Geometric hashing: An overview. IEEE Trans. Comput. Sci. Eng. 4, 4, 10--21. Google ScholarGoogle ScholarDigital LibraryDigital Library
  265. Woodrow, E. and Heinzelman, W. 2002. Spin-It: A data centric routing protocol for image retrieval in wireless networks. In Proceedings of the IEEE International Conference on Image Processing (ICIP).Google ScholarGoogle Scholar
  266. Wu, G., Chang, E. Y., and Panda, N. 2005. Formulating context-dependent similarity functions. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  267. Wu, H., Lu, H., and Ma, S. 2004. Willhunter: Interactive image retrieval with multilevel relevance measurement. In Proceedings of the IEEE International Conference on Pattern Recognition (ICPR). Google ScholarGoogle ScholarDigital LibraryDigital Library
  268. Wu, P. and Manjunath, B. S. 2001. Adaptive nearest neighbor search for relevance feedback in large image databases. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  269. Wu, Y., Chang, E. Y., Chang, K. C. C., and Smith, J. R. 2004. Optimal multimodal fusion for multimedia data analysis. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  270. Wu, Y., Tian, Q., and Huang, T. S. 2000. Discriminant-EM algorithm with application to image retrieval. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Google ScholarGoogle Scholar
  271. Xie, X., Liu, H., Goumaz, S., and Ma, W.-Y. 2005. Learning user interest for image browsing on small-form-factor devices. In Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI). Google ScholarGoogle ScholarDigital LibraryDigital Library
  272. Xing, E., Ng, A., Jordan, M., and Russell, S. 2003. Distance metric learning, with application to clustering with side-information. In Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS).Google ScholarGoogle Scholar
  273. Yang, C., Dong, M., and Fotouhi, F. 2005a. Region based image annotation through multiple-instance learning. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  274. Yang, C., Dong, M., and Fotouhi, F. 2005b. Semantic feedback for interactive image retrieval. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  275. Yee, K.-P., Swearingen, K., Li, K., and Hearst, M. 2003. Faceted metadata for image search and browsing. In Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI). Google ScholarGoogle ScholarDigital LibraryDigital Library
  276. Yu, J., Amores, J., Sebe, N., and Tian, Q. 2006. Toward robust distance metric analysis for similarity estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Google ScholarGoogle ScholarDigital LibraryDigital Library
  277. Yu, S. X. and Shi, J. 2004. Segmentation given partial grouping constraints. IEEE Trans. Pattern Anal. Mach. Intell. 26, 2, 173--183. Google ScholarGoogle ScholarDigital LibraryDigital Library
  278. Zhai, Y., Yilmaz, A., and Shah, M. 2005. Story segmentation in news videos using visual and textual cues. In Proceedings of the ACM International Conference on Multimedia.Google ScholarGoogle Scholar
  279. Zhang, D.-Q. and Chang, S.-F. 2004. Detecting image near-duplicate by stochastic attributed relational graph matching with learning. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  280. Zhang, H., Rahmani, R., Cholleti, S. R., and Goldman, S. A. 2006. Local image representations using pruned salient points with applications to cbir. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  281. Zhang, H. J., Wenyin, L., and Hu, C. 2000. IFIND---A system for semantics and feature based image retrieval over Internet. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  282. Zhang, L., Chen, L., Jing, F., Deng, K., and Ma, W. Y. 2006. Enjoyphoto----A vertical image search engine for enjoying high-quality photos. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  283. Zhang, L., Chen, L., Li, M., and Zhang, H.-J. 2003. Automated annotation of human faces in family albums. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  284. Zhang, Q., Goldman, S. A., Yu, W., and Fritts, J. E. 2002. Content-based image retrieval using multiple-instance learning. In Proceedings of the International Conference on Machine Learning (ICML). Google ScholarGoogle ScholarDigital LibraryDigital Library
  285. Zhang, R. and Zhang, Z. 2004. Hidden semantic concept discovery in region based image retrieval. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Google ScholarGoogle ScholarDigital LibraryDigital Library
  286. Zhang, Y., Brady, M., and Smith, S. 2001. Segmentation of brain mr images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans. Medical Imag. 20, 1, 45--57.Google ScholarGoogle ScholarCross RefCross Ref
  287. Zhao, W., Chellappa, R., Phillips, P. J., and Rosenfeld, A. 2003. Face recognition: A literature survey. ACM Comput. Surv. 35, 4, 399--458. Google ScholarGoogle ScholarDigital LibraryDigital Library
  288. Zheng, B., McClean, D. C., and Lu, X. 2006. Identifying biological concepts from a protein-related corpus with a probabilistic topic model. BMC Bioinf. 7, 58.Google ScholarGoogle ScholarCross RefCross Ref
  289. Zheng, X., Cai, D., He, X., Ma, W.-Y., and Lin, X. 2004. Locality preserving clustering for image database. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  290. Zhou, D., Weston, J., Gretton, A., Bousquet, O., and Scholkopf, B. 2003. Ranking on data manifolds. In Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS).Google ScholarGoogle Scholar
  291. Zhou, X. S. and Huang, T. S. 2001a. Comparing discriminating transformations and svm for learning during multimedia retrieval. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  292. Zhou, X. S. and Huang, T. S. 2001b. Small sample learning during multimedia retrieval using biasmap. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Google ScholarGoogle Scholar
  293. Zhou, X. S. and Huang, T. S. 2002. Unifying keywords and visual contents in image retrieval. IEEE Multimed. 9, 2, 23--33. Google ScholarGoogle ScholarDigital LibraryDigital Library
  294. Zhou, X. S. and Huang, T. S. 2003. Relevance feedback in image retrieval: A comprehensive review. Multimed. Syst. 8, 536--544.Google ScholarGoogle ScholarCross RefCross Ref
  295. Zhu, L., Zhang, A., Rao, A., and Srihari, R. 2000. Keyblock: An approach for content-based image retrieval. In Proceedings of the ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  296. Zhu, S.-C. and Yuille, A. 1996. Region competition: Unifying snakes, region growing, and Bayes/MDL for multiband image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 18, 9, 884--900. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. Image retrieval: Ideas, influences, and trends of the new age

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          cover image ACM Computing Surveys
          ACM Computing Surveys  Volume 40, Issue 2
          April 2008
          130 pages
          ISSN:0360-0300
          EISSN:1557-7341
          DOI:10.1145/1348246
          Issue’s Table of Contents

          Copyright © 2008 ACM

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          Publication History

          • Published: 8 May 2008
          • Accepted: 1 July 2007
          • Revised: 1 June 2007
          • Received: 1 November 2006
          Published in csur Volume 40, Issue 2

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