Eva Hörster
Rainer Lienhart
Malcolm Slaney
ABSTRACT
Online image repositories such as Flickr contain hundreds of millions of images and are growing quickly. Along with that the needs for supporting indexing, searching and browsing is becoming more and more pressing. In this work we will employ the image content as a source of information to retrieve images. We study the representation of images by Latent Dirichlet Allocation (LDA) models for content-based image retrieval. Image representations are learned in an unsupervised fashion, and each image is modeled as the mixture of topics/object parts depicted in the image. This allows us to put images into subspaces for higher-level reasoning which in turn can be used to find similar images. Different similarity measures based on the described image representation are studied. The presented approach is evaluated on a real world image database consisting of more than 246,000 images and compared to image models based on probabilistic Latent Semantic Analysis (pLSA). Results show the suitability of the approach for large-scale databases. Finally we incorporate active learning with user relevance feedback in our framework, which further boosts the retrieval performance.
- R. A. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1999. Google Scholar
Digital Library
- K. Barnard, P. Duygulu, D. Forsyth, N. de Freitas, D. M. Blei, and M. I. Jordan. Matching words and pictures. J. Mach. Learn. Res., 3:1107--1135, 2003. Google ScholarDigital Library
- D. M. Blei and M. I. Jordan. Modeling annotated data. In SIGIR '03: Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, pages 127--134, New York, NY, USA, 2003. ACM Press. Google ScholarDigital Library
- D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. J. Mach. Learn. Res., 3:993--1022, 2003. Google ScholarCross Ref
- A. Bosch, A. Zisserman, and X. Munoz. Scene classification via pLSA. In Proceedings of the European Conference on Computer Vision, 2006. Google ScholarDigital Library
- R. Fergus, L. Fei-Fei, P. Perona, and A. Zisserman. Learning object categories from google's image search. In ICCV '05: Proceedings of the Tenth IEEE International Conference on Computer Vision, pages 1816--1823, Washington, DC, USA, 2005. IEEE Computer Society. Google ScholarDigital Library
- T. Hofmann. Unsupervised learning by probabilistic latent semantic analysis. Mach. Learn., 42(1--2):177--196, 2001. Google ScholarDigital Library
- F.-F. Li and P. Perona. A Bayesian hierarchical model for learning natural scene categories. In CVPR '05: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2, pages 524--531, Washington, DC, USA, 2005. IEEE Computer Society. Google ScholarDigital Library
- R. Lienhart and M. Slaney. pLSA on large scale image databases. In IEEE International Conference on Acoustics, Speech and Signal Processing, 2007.Google ScholarCross Ref
- D. G. Lowe. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision, 60(2):91--110, 2004. Google ScholarDigital Library
- G. Pass, R. Zabih, and J. Miller. Comparing images using color coherence vectors. In MULTIMEDIA '96: Proceedings of the fourth ACM international conference on Multimedia, pages 65--73, New York, NY, USA, 1996. ACM Press. Google ScholarDigital Library
- P. Quelhas, F. Monay, J.-M. Odobez, D. Gatica-Perez, T. Tuytelaars, and L. V. Gool. Modeling scenes with local descriptors and latent aspects. In ICCV '05: Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, pages 883--890, Washington, DC, USA, 2005. IEEE Computer Society. Google ScholarDigital Library
- J. Sivic, B. C. Russell, A. A. Efros, A. Zisserman, and W. T. Freeman. Discovering objects and their location in images. In International Conference on Computer Vision (ICCV 2005), 2005. Google ScholarDigital Library
- E. B. Sudderth, A. B. Torralba, W. T. Freeman, and A. S. Willsky. Describing visual scenes using transformed dirichlet processes. In NIPS, 2005.Google Scholar
- S. Tong and E. Chang. Support vector machine active learning for image retrieval. In MULTIMEDIA '01: Proceedings of the ninth ACM international conference on Multimedia, pages 107--118, New York, NY, USA, 2001. ACM Press. Google ScholarDigital Library
- G. Wang, Y. Zhang, and L. Fei-Fei. Using dependent regions for object categorization in a generative framework. In CVPR '06: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 1597--1604, Washington, DC, USA, 2006. IEEE Computer Society. Google ScholarDigital Library
- X. Wei and W. B. Croft. LDA-based document models for ad-hoc retrieval. In SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pages 178--185, New York, NY, USA, 2006. ACM Press. Google ScholarDigital Library
- C. Zhai and J. Lafferty. A study of smoothing methods for language models applied to ad hoc information retrieval. In SIGIR '01: Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, pages 334--342, New York, NY, USA, 2001. ACM Press. Google ScholarDigital Library
Index Terms
- Image retrieval on large-scale image databases
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