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Multi-view multi-label active learning for image classification

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5 Author(s)
Xiaoyu Zhang ; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China ; Jian Cheng ; Changsheng Xu ; Hanqing Lu
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Image classification is an important topic in multimedia analysis, among which multi-label image classification is a very challenging task with respect to the large demand for human annotation of multi-label samples. In this paper, we propose a multi-view multi-label active learning strategy, which integrates the mechanism of active learning and multi-view learning. On one hand we explore the sample and label uncertainties within each view; on the other hand we capture the uncertainty over different views based on multi-view fusion. Then the overall uncertainty along the sample, label and view dimensions are obtained to detect the most informative sample-label pairs. Experimental results demonstrate the effectiveness of the proposed scheme.

Published in:

2009 IEEE International Conference on Multimedia and Expo

Date of Conference:

June 28 2009-July 3 2009