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Boost SVM active learning for content-based image retrieval

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3 Author(s)
Wei Jiang ; Dept. of Autom., Tsinghua Univ., Beijing, China ; Guihua Er ; Qionghai Dai

Content-based image retrieval (CBIR) can be viewed as a classification problem, and the classical support vector machine active learning (SVMActive) algorithm gives a satisfactory solution. In this paper, based on the SVMActive algorithm, our contribution is: boosting method is incorporated with SVMActive to get the Boost SVMActive (BSVMActive) algorithm. Following the basic sample re-weighting idea of AdaBoost, we modify this method to be adaptive to CBIR problem. Boosting method can improve the performance of SVMActive classifier with both higher accuracy and faster training process. Experiment results over three different scales datasets show that our new method can achieve consistently higher performance than original SVMActive.

Published in:

Signals, Systems and Computers, 2004. Conference Record of the Thirty-Seventh Asilomar Conference on  (Volume:2 )

Date of Conference:

9-12 Nov. 2003

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