ABSTRACT
A bigram correlation model for image retrieval is proposed, which captures the semantic relationship among images in a database from simple statistics of users' relevance feedback information. It is used in the post-processing of image retrieval results such that more semantically related images are returned to the user. The algorithm is easy to implement and can be efficiently integrated into an image retrieval system to help improve the retrieval performance. Preliminary experimental results on a database of 100,000 images are very promising.
- Aksoy, S. and Haralick, R.M., "Graph-theoretic clustering for image grouping and retrieval", in Proc. IEEE CVPR, 1999.Google ScholarCross Ref
- Chen, Z. et al., "Web Mining for Web Image Retrieval", to appear in the special issue of Journal of the American Society for Information Science on Visual Based Retrieval Systems and Web Mining. Google ScholarDigital Library
- Clarkson, P.R. and Rosenfeld, R., "Statistical Language Modeling Using the CMU-Cambridge Toolkit", in Proc. ESCA Eurospeech, 1997.Google Scholar
- Cox, I.J. et al, "The Bayesian Image Retrieval System, PicHunter: Theory, Implementation and Psychophysical Experiments", IEEE Trans. on Image Processing, Vol. 9, No. 1, Jan. 2000. Google ScholarDigital Library
- Huang, J. et al, "Combining Supervised Learning with Color Correlograms for Content-Based Image Retrieval", in Proc. ACM Multimedia, 1997. Google ScholarDigital Library
- Lee, C.S., Ma, W.Y., and Zhang, H.J., "Information Embedding Based on User's Relevance Feedback for Image Retrieval", invited paper, SPIE Symposium on Voice, Video and Data Communications, 1999.Google Scholar
- Lu, Y. et al, "A Unified Framework for Semantics and Feature Based Relevance Feedback in Image Retrieval Systems", in Proc. ACM Multimedia, 2000. Google ScholarDigital Library
- Rui, Y. et al, "A Relevance Feedback Architecture for Content-Based Multimedia Information Retrieval Systems", in Proc. IEEE Workshop on content-Based Access of Image and Video Libraries, 1997. Google ScholarDigital Library
- Zhang, H.J. et al, "iFind---A System for Semantics and Feature Based Image Retrieval over Internet", in Proc. ACM Multimedia, 2000. Google ScholarDigital Library
Index Terms
- A statistical correlation model for image retrieval
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