Performance comparison of different similarity models for CBIR with relevance feedback

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

Volumn 2728, Issue , 2003, Pages 456-466

  Heesch, Daniel ^a   Yavlinsky, Alexei ^a   Rüger, Stefan ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

CONTENT BASED RETRIEVAL; FEEDBACK; INFORMATION RETRIEVAL; NEAREST NEIGHBOR SEARCH; VECTOR SPACES;

COMPARATIVE STUDIES; CONTENT BASED IMAGE RETRIEVAL; K-NEAREST NEIGHBOURS; LEARNING TECHNIQUES; PERFORMANCE COMPARISON; RELEVANCE FEEDBACK; RETRIEVAL PERFORMANCE; VECTOR SPACE MODELS;

IMAGE RETRIEVAL;

EID: 35248873946     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45113-7_45     Document Type: Review

References (16)

1. Y Chen, X Zhou, and T S Huang. One-class SVM for learning in image retrieval. In Proc IEEE Image processing 2001, 2001.
2. I J Cox, M L Miller, T P Minka, T V Papathomas, and P N Yianilos. The Bayesian image retrieval system, pichunter. IEEE Transactions on Image Processing, 9(1):20-38, 2000.
3. B V Dasarathy, editor. Nearest Neigbour (NN) Norms: NN Pattern Classification Techniques. IEEE Computer Society Press, 1991.
4. D Heesch and S Rüger. Performance boosting with three mouse clicks - relevance feedback for CBIR. In Proceedings of the European Colloquium on IR Research 2003. LNCS, Springer, 2003.
5. T S Huang. Incorporating support vector machines in content-based image retrieval with relevance feedback. In Proc IEEE Image processing 2000, 2000.
6. B S Manjunath and J-R Ohm. Color and texture descriptors. IEEE Transactions on circuits and systems for video technology, 11:703-715, 2001.
7. C Meilhac and C Nastar. Relevance feedback and category search in image databases. In Proc. IEEE Int. Conf. Multimedia Comp. and Syst., pages 512-517, 1999.
8. T M Mitchell. Machine Learning. McGraw Hill, 1997.
9. H Müller, S Marchand-Maillet, and T Pun. The truth about Corel - evaluation in image retrieval. In Proceedings of CIVR, pages 38-49, 2002.
10. H Müller, W Müller, D M Squire, M.S Marchand-Maillet, and T Pun. Strategies for positive and negative relevance feedback in image retrieval. In Proceedings of the 15th International Conference on Pattern Recognition (ICPR 2000), IEEE, Barcelona, Spain, 2000.
11. M Pickering and S Rüger. Evaluation of key-frame based retrieval techniques for video, submitted to Elsevier Science and accepted for publication, 2003.
12. Y Rui and T S Huang. A novel relevance feedback technique in image retrieval. In ACM Multimedia (2), pages 67-70, 1999.
13. G Salton and M J Gill. Introduction to Modern Information Retrieval. McGraw-Hill Book Co., 1983.
14. B Schölkopf, J C Platt, J T Shawe, A J Smola, and R C Williamson. Estimation the support of a high-dimensional distribution. Technical Report MSR-TR-99-87, Microsoft Research, 1999.
15. V N Vapnik. The Nature of Statistical Learning Theory. Springer Verlag, 1995.
16. E M Voorhees and D Herman. Overview of the eigth Text REtrieval Conference (TREC-8). In Proc. TREC, pages 1-33 and A.17-A.18, 1999.