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Knowledge Based Systems Technology and Applications in Image Retrieval

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Book cover Intelligent Knowledge-Based Systems

Abstract

Visual Languages can be basically classified in two main categories: languages that provide a formalism for visual representation and languages for visual programming. To the first class belong languages that provide a logical interpretation of visual information such as images or pictorial objects. To the second class belong languages that support a visual representation of traditional data type to provide systems with a more user-oriented interface.

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References

  1. Aiello, M. 2001. Computing spatial similarity by games In Esposito, E, Proceedings of the Eighth Conference of the Italian Association for Artificial Intelligence (AI*IA’99), 2175 in Lecture Notes in Artificial Intelligence, 99–110. Springer-Verlag.

    Google Scholar 

  2. Ardizzone, E., Chella, A., Gaglio, S. 1997. Hybrid computation and reasoning for artificial vision In Cantoni, V., Levialdi, S., Roberto, V., Artificial Vision, 193–221. Academic Press.

    Google Scholar 

  3. Baader, F. Hanschke, P. 1991. A schema for integrating concrete domains into concept languages In Proceedings of the Twelfth International Joint Conference on Artificial Intelligence (IJCAI’91), 452–457, Sydney.

    Google Scholar 

  4. Baader, F., Calvanese, D., McGuinness, D., Nardi, D., Patel-Schneider, P. Editors 2003. The Description Logic Handbook, Theory, Implementation and Applications. Cambridge.

    Google Scholar 

  5. Bach, R., Fuller, C., Gupta, A., Hampapur, A., Horowitz, B., Humphrey, R., Jain, R., Shu, C. 1996. The Virage image search engine: an open framework for image management In Storage and Retrieval for Image and Video Databases, 2670, 76–87. SPIE.

    Google Scholar 

  6. Bertino, E. Catania, B. 1998. A constraint-based approach to shape management in multimedia databases MultiMedia Systems, 6, 2–16.

    Google Scholar 

  7. Del Bimbo A. Visual Information Retrieval. 1999. Morgan Kaufmann Publisher

    Google Scholar 

  8. Borgida, A. 1995. Description Logics in Data Management. IEEE Transactions on Transactions on Knowledge and Data Engineering, 7(5), 671–682.

    Google Scholar 

  9. Brooks, R. 1981. Symbolic reasoning among 3-D models and 2-D images Artificial Intelligence, 17, 285–348.

    Google Scholar 

  10. Calvanese, D., Lenzerini, M., Nardi, D. 1998. Description logics for conceptual data modeling In Chomicki, J. Saake, G., Logics for Databases and Information Systems, 229–264. Kluwer Academic Publisher.

    Google Scholar 

  11. Cardoze, D. Schulman, L. 1998. Pattern matching for spatial point sets In Proceedings of the Thirtyninth Annual Symposium on the Foundations of Computer Science (FOCS’98), 156–165, Palo Alto, CA.

    Google Scholar 

  12. Carson, C., Thomas, M., Belongie, S., Hellerstein, J. M., Malik, J. 1999. Blobworld: A system for region-based image indexing and retrieval In Huijsmans, D. Smeulders, A., Lecture Notes in Computer Science, 1614, 509–516. Springer-Verlag.

    Google Scholar 

  13. Celentano, A. Di Sciascio, E. 1998. Features integration and relevance feedback analysis in image similarity evaluation Journal of Electronic Imaging, 7 (2), 308–317.

    Google Scholar 

  14. Chandra, A. Harel, D. 1980. Computable queries for relational databases Journal of Computer and System Sciences, 21, 156–178.

    Google Scholar 

  15. Chang, S., Shi, Q., Yan, C. 1983. Iconic indexing by 2D strings IEEE Transactions on Pattern Analysis and Machine Intelligence, 9 (3), 413–428.

    Google Scholar 

  16. Chew, L., Goodrich, M., Huttenlocher, D., Kedem, K., Kleinberg, J., Kravets, D. 1997. Geometric pattern matching under euclidean motion Computational Geometry, 7, 113–124.

    Google Scholar 

  17. Cox, I., Miller, M., Minka, T., Papathomas, T. 2000. The bayesian image retrieval system, PicHunter IEEE Transactions on Image Processing, 9 (1), 20–37.

    Google Scholar 

  18. Di Sciascio, E., Donini, F. M., Mongiello, M. 2000. A Description logic for image retrieval In Lamma, E. Mello, P., AI*IA 99: Advances in Artificial Intelligence, 1792 in Lecture Notes in Artificial Intelligence, 13–24. Springer-Verlag.

    Google Scholar 

  19. Di Sciascio, E., Donini, F. M., Mongiello, M. 2002. Spatial layout representation for query-by-sketch content-based image retrieval. Pattern Recognition Letters, Elsevier, 23(13), 1599–1612.

    Google Scholar 

  20. Di Sciascio, E., Donini, F. M., Mongiello, M. 2002. A logic for SVG documents query and retrieval In Proceedings of International Workshop on Multimedia Semantics (SOFSEM 2002), Milovy, Czech Republic, November 28–29.

    Google Scholar 

  21. Di Sciascio, E., Donini, F. M., Mongiello, M. 2002. Structured Knowledge Representation for Image Retrieval Journal of Artificial Intelligence Research, 16, 209–257, Morgan-Kaufmann.

    Google Scholar 

  22. Di Sciascio, E. Mongiello, M. 1999. Query by sketch and relevance feedback for content-based image retrieval over the web, Journal of Visual Languages and Computing, 10 (6), 565–584.

    Google Scholar 

  23. Donini, F., Lenzerini, M., Nardi, D., Schaerf, A. 1996. Reasoning in description logics In Brewka, G., Foundations of Knowledge Representation, 191–236. CSLI-Publications.

    Google Scholar 

  24. Edelmann, S. 1999. Representation and Recognition in Vision. The MIT Press.

    Google Scholar 

  25. EI-Kwae, E. Kabuka, M. 1999. Content-based retrieval by spatial similarity in image databases ACM Transactions on Information Systems, 17, 174–198.

    Google Scholar 

  26. Flickner, M., Sawhney, H., Niblak, W, Ashley, J., Huang, Q., Dom, B., Gorkani, M., Hafner, J., Lee, D., Petkovic, D., Steele, D., Yanker, P. 1995. Query by image and video content: The QBIC system IEEE Computer, 28 (9), 23–31.

    Google Scholar 

  27. Foley, J., van Dam, A., Feiner, S., Hughes, J. 1996. Computer Graphics. Addison Wesley Publ. Co., Reading, Massachussetts.

    Google Scholar 

  28. Fuhr, N., Gövert, N., Rölleke, T. 1998. DOLORES: A system for logic-based retrieval of multimedia objects In Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Developement in Information Retrieval (SIGIR’ 98), 257–265, Melbourne, Australia.

    Google Scholar 

  29. Gevers, T. Smeulders, A. 2000. Pictoseek: Combining color and shape invariant features for image retrieval IEEE Transactions on Image Processing, 9 (1), 102–119.

    Google Scholar 

  30. Gudivada, V. 1998. θ R-string: A geometry-based representation for efficient and effective retrieval of images by spatial similarity IEEE Transactions on Knowledge and Data Engineering, 10 (3), 504–512.

    Google Scholar 

  31. Gudivada, V. Raghavan, J. 1995. Design and evaluation of algorithms for image retrieval by spatial similarity ACM Transactions on Information Systems, 13 (2), 115–144.

    Google Scholar 

  32. Haarslev, v., Lutz, C., Möeller, R. 1998. Foundations of spatioterminological reasoning with description logics In Proceedings of the Sixth International Conference on Principles of Knowledge Representation and Reasoning (KR’98), 112–123.

    Google Scholar 

  33. Hacid, M.-S. Rigotti, C. 1999. Representing and reasoning on conceptual queries over image databases In Proceedings of the Twelfth International Symposium on Methodologies for Intelligent Systems (ISMIS’99), 1609 in Lecture Notes in Artificial Intelligence, 340–348, Warsaw, Poland. Springer-Verlag.

    Google Scholar 

  34. Hartman, J. Wernecke, J. 1996. The VRML 2.0 Handbook. Addison-Wesley.

    Google Scholar 

  35. Hirata, K. Kato, T. 1992. Query by visual example In Pirotte, A., Delobel, C., Gottlob, G., Advances in Database Technology—Proc. 3rd Int. Conf. Extending Database Technology, EDBT, 580 of Lecture Notes in Computer Science, 56–71. Springer-Verlag.

    Google Scholar 

  36. Jacobs, C., Finkelstein, A., Salesin, D. 1995. Fast multiresolution image querying In Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH’ 95), 277–286.

    Google Scholar 

  37. Jahne, B., Haubecker, H., Geibler, P 1999. Handbook of Computer Vision and Applications. Academic Press.

    Google Scholar 

  38. Ma, W Manjunath, B. 1997. NETRA: A toolbox for navigating large image database In Proceedings of the IEEE International Conference on Image Processing (ICIP’ 97), 1, 568–571, Santa Barbara.

    Google Scholar 

  39. Marr, D. 1982. Vision. W.H. Freeman and Co., Oxford.

    Google Scholar 

  40. Meghini, C., Sebastiani, F., Straccia, U. 2001. A model of multimedia information retrieval Journal of the ACM, 48 (5), 909–970.

    Google Scholar 

  41. Moeller, R., Neumann, B., Wessel, M. 1999. Towards computer vision with description logics: some recent progress In Proceedings of the IEEE Integration of Speech and Image Understanding, 101–115.

    Google Scholar 

  42. Nebel, B. 1990. Reasoning and Revision in Hybrid Representation Systems. 422 in Lecture Notes in Artificial Intelligence. Springer-Verlag.

    Google Scholar 

  43. Niblak, W, Barder, R., Equitz, W, Flickner, M., Glasman, E., Petkovic, D., Yanker, P., Faloustos, C. 1993. The QBIC project: Querying images by content using color, texture, and shape In Storage and Retrieval for Still Image and Video Databases, 1980, 173–182. SPIE.

    Google Scholar 

  44. Paquet, E. Rioux, M. 1998. A content-based search engine for VRML databases In Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR’98), 541–546, Santa Barbara, CA.

    Google Scholar 

  45. Picard, R. Kabir, T. 1993. Finding similar patterns in large image databases In Proceedings of the IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP’ 93), 161–164, Minneapolis, MN.

    Google Scholar 

  46. Pirri, F. Finzi, A. 1999. An approach to perception in theory of actions: part 1 In Linkoping Electronic Articles in Computer and Information Science, 41. Linkoping University Electronic Press.

    Google Scholar 

  47. Pok, G. Liu, J. 1999. Texture classification by a two-level hybrid scheme In Storage and Retrieval for Image and Video Databases VII, 3656, 614–622. SPIE.

    Google Scholar 

  48. Pratt, W. 1991. Digital Image Processing. J. Wiley & Sons Inc., Englewood Cliffs, NJ.

    Google Scholar 

  49. Reiter, R. Mackworth, A. 1989. A logical framework for depiction and image interpretation Artificial Intelligence, 41 (2), 125–155.

    Google Scholar 

  50. Reiter, R. 1980. Equality and domain closure in first-order databases Journal of the ACM, 27 (2), 235–249.

    Google Scholar 

  51. Rui, Y., Huang, T., Mehrotra, S. 1997. Content-based image retrieval with relevance feedback in MARS In Proceedings of the IEEE International Conference on Image Processing (ICIP’ 97), 815–818.

    Google Scholar 

  52. Rui, Y., She, A., Huang, T. 1996. Modified Fourier descriptors for shape representation—a practical approach In Proceedings of 1st Workshop on Image Databases and Multimedia Search, Amsterdam.

    Google Scholar 

  53. Sanfeliu, A. Fu, K. 1983. A distance measure between attributed relational graphs for pattern recognition IEEE Transactions on Systems, Man, and Cybernetics, 13 (3),353–362.

    Google Scholar 

  54. Schmidt-Schauß, M. Smolka, G. 1991. Attributive Concept Descriptions with Complements, Artificial Intelligence, 48 (1), 1–26.

    Google Scholar 

  55. Smith, J. Chang, S. 1996. VisuaISEEK: a fully automated content-based image query system In Proceedings of the fourth ACM International Conference on Multimedia (Multimedia’96), 87–98.

    Google Scholar 

  56. Straccia, U. 2001. Reasoning within fuzzy description logics Journal of Artificial Intelligence Research, 14, 137–166.

    Google Scholar 

  57. Tagare, H., Vos, F., Jaffe, C., Duncan, J. 1995. Arrangement: A spatial relation between parts for evaluating similarity of tomographic section IEEE Transactions on Pattern Analysis and Machine Intelligence, 17 (9), 880–893.

    Google Scholar 

  58. Ullman, J. D. 1988. Principles of Database and Knowledge Base Systems, 1. Computer Science Press, Potomac, Maryland.

    Google Scholar 

  59. Woods, W A. Schmolze, J. G. 1992. The KL-ONE family. In Lehmann, F. W, Semantic Networks in Artificial Intelligence, 133–178. Pergamon Press. Published as a special issue of Computers & Mathematics with Applications, 23, 2–9.

    Google Scholar 

  60. Yen, J. 1991. Generalizing term subsumption languages to Fuzzy logic In Proceedings of the Twelfth International Joint Conference on Artificial Intelligence (IJCAI’91), 472–477.

    Google Scholar 

  61. Zadeh, L. 1965. Fuzzy sets Information and Control, 8, 338–353.

    Google Scholar 

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Di Sciascio, E., Donini, F.M., Mongiello, M. (2005). Knowledge Based Systems Technology and Applications in Image Retrieval. In: Leondes, C.T. (eds) Intelligent Knowledge-Based Systems. Springer, Boston, MA. https://doi.org/10.1007/978-1-4020-7829-3_11

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  • DOI: https://doi.org/10.1007/978-1-4020-7829-3_11

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