Content-Based Image Retrieval By Relevance Feedback
Relevance feedback is a powerful technique for content-based image retrieval. Many parameter estimation approaches have been proposed for relevance feedback. However, most of them have only utilized information of the relevant retrieved images, and have given up, or have not made great use of information of the irrelevant retrieved images. This paper presents a novel approach to update the interweights of integrated probability function by using the information of both relevant and irrelevant retrieved images. Experimental results have shown the effectiveness and robustness of our proposed approach, especially in the situation of no relevant retrieved images.
KeywordsImage Retrieval Query Image Relevance Feedback Retrieval Performance Invariant Moment
Unable to display preview. Download preview PDF.
- 2.C. P. Lam, J. K. Wu, and B. Mehtre. STAR-A System for trademark archival and retrieval. In 2nd Asian Conf. on Computer Vision, volume 3, pages 214–217, 1995.Google Scholar
- 3.Y. S. Kim and W. Y. Kim. Content-based trademark retrieval system using visually salient feature. In IEEE Computer Society Cnf. on Computer Vision and Pattern Recognition, pages 307–312, 1997.Google Scholar
- 6.D. Y. M. Chan, I. King, D. P. Huijsmans et al. Genetic algorithm for weights assignment in dissimilarity function for trademark retrieval. In Visual Information and Information Systems. Third International Conference, VISUAL’99. Proceedings (Lecture Notes in Computer Science Vol.1614), pages 557–565, The Netherlands, June 1999.Google Scholar
- 7.Y. Rui, T. S. Huang, and S. Mehrotra. Content-based image retrieval with relevance feedback in MARS. Proceedings of IEEE International Conference on Image Processing,pp815–818, Santa Barbara, California, October, 1997.Google Scholar
- 8.I. J. Aalbersberg. Incremental relevance feedback. 15th International ACM/SIGIR Conference on Research and Development in Information Retrieval, Demark, June, 1992, pp11–22Google Scholar
- 9.C. Lundquist, D. A. Grossman, and O. Frieder. Improving relevance feedback in the vector space model. Proceedings of the Sixth International Conference on Information and Knowledge Management. CIKM’97, Las Vegas, Nevada, USA, Nov. 1997, 16–23Google Scholar
- 10.C Buckley and G. Salton. Optimization of relevance feedback weights. 18th International ACM SIGIR Conference on Research and Development in Information Retrieval, Seattle, WA, USA, July 1995, 351–357.Google Scholar
- 11.Y. Ishikawa, R. Subramanya, C. Faloutsos et al. Mindreader: querying database through multiple examples. Proceedings of the Twenty-Fourth International Conference on Very-Large Databases, New York, Aug. 1998, pp218–227.Google Scholar
- 13.Y. Rui and T. S. Huang. A novel relevance feedback technique in image retrieval. Proceedings ACM Multimedia’ 99(Part2),Orlando, FL, USA, 1999, pp67–70.Google Scholar
- 14.M. E. J. Wood, N. W. Campbell and B. T. Thomas. Iterative refinement by relevance feedback in content-based digital image retrieval. Proceedings ACM Multimedia’98, Bristol, UK, Sept, 1998, pp13–18Google Scholar
- 15.I. King, Z. Jin and D. Y. M. Chan. Chinese cursive script character image retrieval based on an integrated probability function, submitted to the VISUAL 2000, 4th International Conference on Visual Information Systems, Lyon, France, November 2–4, 2000Google Scholar