Vector-valued multi-view semi-supervised learning for multi-label image classification

Publication Type:
Conference Proceeding
Citation:
Proceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013, 2013, pp. 647 - 653
Issue Date:
2013-12-01
Metrics:
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2013003219OK.pdf1.23 MB
Adobe PDF
Images are usually associated with multiple labels and comprised of multiple views, due to each image containing several objects (e.g. a pedestrian, bicycle and tree) and multiple visual features (e.g. color, texture and shape). Currently available tools tend to use either labels or features for classification, but both are necessary to describe the image properly. There have been recent successes in using vector-valued functions, which construct matrix-valued kernels, to explore the multi-label structure in the output space. This has motivated us to develop multi-view vector-valued manifold regularization (MV3MR) in order to integrate multiple features. MV3MR exploits the complementary properties of different features, and discovers the intrinsic local geometry of the compact support shared by different features, under the theme of manifold regularization. We validate the effectiveness of the proposed MV3MR methodology for image classification by conducting extensive experiments on two challenge datasets, PASCAL VOC' 07 and MIR Flickr. Copyright © 2013, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Please use this identifier to cite or link to this item: