Active learning for image retrieval with Co-SVM

12 years 9 months ago
Active learning for image retrieval with Co-SVM
In relevance feedback algorithms, selective sampling is often used to reduce the cost of labeling and explore the unlabeled data. In this paper, we proposed an active learning algorithm, Co-SVM, to improve the performance of selective sampling in image retrieval. In Co-SVM algorithm, color and texture are naturally considered as sufficient and uncorrelated views of an image. SVM classifiers are learned in color and texture feature subspaces, respectively. Then the two classifiers are used to classify the unlabeled data. These unlabeled samples which are differently classified by the two classifiers are chose to label. The experimental results show that the proposed algorithm is beneficial to image retrieval. ᭧ 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Jian Cheng, Kongqiao Wang
Added 27 Dec 2010
Updated 27 Dec 2010
Type Journal
Year 2007
Where PR
Authors Jian Cheng, Kongqiao Wang
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