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Statistical learning for effective visual information retrieval

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4 Author(s)
Chang, E.Y. ; Dept. of Electr. & Comput. Eng., California Univ., Santa Barbara, CA, USA ; Beitao Li ; Gang Wu ; Kingshy Goh

For effective retrieval of visual information, statistical learning plays a pivotal role. Statistical learning in such a context faces at least two major mathematical challenges: scarcity of training data, and imbalance of training classes. We present these challenges and outline our methods for addressing them: active learning, recursive subspace co-training, adaptive dimensionality reduction, class-boundary alignment, and quasi-bagging.

Published in:

Image Processing, 2003. ICIP 2003. Proceedings. 2003 International Conference on  (Volume:3 )

Date of Conference:

14-17 Sept. 2003