Edward Chang
ABSTRACT
We describe the Perception-Based Image Retrieval (PBIR) system that we have built on our recently developed query-concept learning algorithms, MEGA and SVMActive. We show that MEGA and SVMActive can learn a complex image-query concept in a small number of user iterations (usually three to four) on a large, multi-category, high-dimensional image database.
- 1.E. Chang. Support vector machlnesfor high-dlmensionalindexing. Technical Report http:/,6vww-db.stanford.edu/~echang/svmdex.ps, May 2001.Google Scholar
- 2.E. Chang and B, Li. Mega-- the maximizing expected generalization algorithm for learning complex query concepls (extended version). TechnicalReport http:/Avww-db.stanford.edu/~chang/mega, Feb, 2001.Google Scholar
- 3.E, Chang, B. Li, and C. Li, Towards perception-basedimage retrieval. IEEE Content Based Access qf hnage and Video 1)'braries, pages 101-t05,June 2000 Google ScholarDigital Library
- 4.K. S. Jones and E W. (Editors). Readbigs in InformationRetrieval. Morgan Kaufinan, July 1997.Google Scholar
- 5.B. Li, E. Chang, and C,-S, Li. Learning image query concepts via intelligent sampling, Proceedings oflEEE Multbnedio, August 2001Google Scholar
- 6.C. Li, E. Chang, H. Garcia-Molina, and G, Wiederhold. Clustering for approximate similarity queries in high-dimensional spaces. IEEE Transaction on Knowh, dge and Data Engineering (to appear), 2001. Google ScholarDigital Library
- 7.T. M. Mitchell. Maddne Learnning McGraw-Hill, 1997.Google Scholar
- 8.K. Porkaew, K. Chakrabarti, and S. Mehrotra. Query refinement for multimedia similarity retrieval in mars. Proceedings of ACM Multimedia, November 1999. Google ScholarDigital Library
- 9.S. Tong and E. Chang. Support vector machine active teaming for image retrieval. Technical Report tittp ://www-db.stanford.edld/~chang/svmactive, ps, March 2001Google Scholar
- 10.L, Wu, C, Faloutsos, K. Sycara, and T. R. Payne. Falcon: Feedback adaptive loop for contem-based retrieval, The 26th VLDB Conference, September 2000. Google ScholarDigital Library
Index Terms
- PBIR: perception-based image retrieval-a system that can quickly capture subjective image query concepts
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