Fast query point movement techniques with relevance feedback for content-based image retrieval

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3896 LNCS, Issue , 2006, Pages 700-717

  Liu, Danzhou ^a   Hua, Kien A ^a   Vu, Khanh ^a   Yu, Ning ^a  

^a University of Central Florida   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SELECTION AND EVALUATION; COMPUTER SIMULATION; CONTENT BASED RETRIEVAL; FEEDBACK; IMAGE PROCESSING; ITERATIVE METHODS;

QUERY POINTS; QUERY REFINEMENT; RETRIEVAL PERFORMANCE; TARGET SEARCH;

QUERY LANGUAGES;

EID: 33745714816     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11687238_42     Document Type: Conference Paper

References (23)

1. P. Browne and A. F. Smeaton. Video Information Retrieval Using Objects and Ostensive Relevance Feedback. In Proceedings of the ACM Symposium on Applied Computing (SAC), pages 1084-1090, 2004.
2. C. Carson, S. Belongie, H. Greenspan, and J. Malik. Blobworld: image segmentation using expectation-maximization and its application to image querying. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(8):1026-1038, 2002.
3. K. Chakrabarti, O.-B. Michael, S. Mehrotra, and K. Porkaew. Evaluating refined queries in top-k retrieval systems. IEEE Transactions on Knowledge and Data Engineering, 16(2):256-270, 2004.
4. I. J. Cox, M. L. Miller, T. P. Minka, T. V. Papathomas, and P. N. Yianilos. The Bayesian image retrieval system, PicHunter: theory, implementation, and psychophysical experiments. IEEE Transactions on Image Processing, 9(1):20-37, 2000.
5. M. Flickner, H. S. Sawhney, J. Ashley, Q. Huang, B. Dom, M. Gorkani, J. Hafner, D. Lee, D. Petkovic, D. Steele, and P. Yanker. Query by image and video content: The QBIC system. IEEE Computer, 28(9):23-32, 1995.
6. T. Gevers and A. Smeulders. Content-based image retrieval: An overview. In G. Medioni and S. B. Kang, editors, Emerging Topics in Computer Vision. Prentice Hall, 2004.
7. K. A. Hua, N. Yu, and D. Liu. Query Decomposition: A Multiple Neighborhood Approach to Relevance Feedback Processing in Content-based Image Retrieval. In Proceedings of the IEEE ICDE Conference, 2006.
8. Y. Ishikawa, R. Subramanya, and C. Faloutsos. MindReader: Querying databases through multiple examples. In Proceedings of the 24th VLDB Conference, pages 218-227, 1998.
9. D.-H. Kim and C.-W. Chung. Qcluster: relevance feedback using adaptive clustering for content-based image retrieval. In Proceedings of the ACM SIGMOD Conference, pages 599-610, 2003.
10. D. Liu, K. A. Hua, K. Vu, and N. Yu. Efficient Target Search with Relevance Feedback for Large CBIR Systems. In Proceedings of the 21st Annual ACM Symposium on Applied Computing, 2006.
11. O.-B. Michael and S. Mehrotra. Relevance feedback techniques in the MARS image retrieval systems. Multimedia Systems, (9):535-547, 2004.
12. M. Nakazato, L. Manola, and T. S. Huang. ImageGrouper: a group-oriented user interface for content-based image retrieval and digital image arrangement. Journal of Visual Languages and Computing, 14(4):363-386, 2003.
13. F. P. Preparata and M. I. Shamos. Computational Geometry: An Introduction. Springer-Verlag, New York Inc., 1985.
14. Y. Rui, T. Huang, M. Ortega, and S. Mehrotra. Relevance feedback: A power tool for interactive content-based image retrieval. IEEE Transactions on Circuits and Systems for Video Technology, 8(5):644-655, 1998.
15. H. T. Shen, B. C. Ooi, and X. Zhou. Towards effective indexing for very large video sequence database. In Proceedings of the ACM SIGMOD Conference, pages 730-741, 2005.
16. A. W. M. Smeulders, M. Worring, A. G. S. Santini, and R. Jain. Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(12):1349-1380, 2000.
17. J. R. Smith and S.-F. Chang. Transform features for texture classification and discrimination in large image databases. In Proceedings of the International Conference on Image Processing, pages 407-411, 1994.
18. J. R. Smith and S.-F. Chang. VisualSEEk: A fully automated content-based image query system. In Proceedings of the 4th ACM Multimedia Conference, pages 87-98, 1996.
19. M. A. Stricker and M. Orengo. Similarity of color images. In Proceedings of Storage and Retrieval for Image and Video Databases (SPIE), pages 381-392, 1995.
20. K. Vu, K. A. Hua, and W. Tavanapong. Image retrieval based on regions of interest. IEEE Transactions on Knowledge and Data Engineering, 15(4):1045-1049, 2003.
21. J. Z. Wang, J. Li, and G. Wiederhold. SIMPLIcity: Semantics-sensitive integrated matching for picture libraries. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(9):947-963, 2001.
22. L. Wu, C. Faloutsos, K. Sycara, and T. R. Payne. FALCON: feedback adaptive loop for content-based retrieval. In Proceedings of the 26th VLDB Conference, pages 297-306, 2000.
23. X. S. Zhou and T. S. Huang. Edge-based structural features for content-based image retrieval. Pattern Recognition Letters, 22(5):457-468, 2001.