Image annotation by multiple-instance learning with discriminative feature mapping and selection

Publication Type:
Journal Article
IEEE Transactions on Cybernetics, 2014, 44 (5), pp. 669 - 680
Issue Date:
Filename Description Size
06542696.pdfPublished Version10.62 MB
Adobe PDF
Full metadata record
Multiple-instance learning (MIL) has been widely investigated in image annotation for its capability of exploring region-level visual information of images. Recent studies show that, by performing feature mapping, MIL can be cast to a single-instance learning problem and, thus, can be solved by traditional supervised learning methods. However, the approaches for feature mapping usually overlook the discriminative ability and the noises of the generated features. In this paper, we propose an MIL method with discriminative feature mapping and feature selection, aiming at solving this problem. Our method is able to explore both the positive and negative concept correlations. It can also select the effective features from a large and diverse set of low-level features for each concept under MIL settings. Experimental results and comparison with other methods demonstrate the effectiveness of our approach. © 2013 IEEE.
Please use this identifier to cite or link to this item: