A boosting SVM chain learning for visual information retrieval

Authors:
Zejian Yuan;Lei Yang;Yanyun Qu;Yuehu Liu;Xinchun Jia
Affiliations:
Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, P.R. China;Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, P.R. China;Department of Computer Science, Xiamen University, Xiamen, P.R. China;Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, P.R. China;Department of Mathematics, Shanxi University, Taiyuan, P.R. China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Year:
2006

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Abstract

Training strategy for negative sample collection and robust learning algorithm for large-scale samples set are critical issues for visual information retrieval problem. In this paper, an improved one class support vector classifier (SVC) and its boosting chain learning algorithm is proposed. Different from the one class SVC, this algorithm considers negative samples information, and integrates the bootstrap training and boosting algorithm into its learning procedure. The performances of the SVC can be successively boosted by repeat important sampling large negative set. Compared with traditional methods, it has the merits of higher detection rate and lower false positive rate, and is suitable for object detection and information retrieval. Experimental results show that the proposed boosting SVM chain learning method is efficient and effective.