Organizing images into semantic categories can be extremely useful for content-based image retrieval and image annotation. Grouping images into semantic classes is a difficult problem, however. Image classification attempts to solve this hard problem by using low-level image features. In this paper, we propose a method for hierarchical classification of images via supervised learning. This scheme relies on using a good low-level feature and subsequently performing feature-space reconfiguration using singular value decomposition to reduce noise and dimensionality. We use the training data to obtain a hierarchical classification tree that can be used to categorize new images. Our experimental results suggest that this scheme not only performs better than standard nearest-neighbor techniques, but also has both storage and computational advantages.
This article is part of the series Unstructured Information Management from Multimedia Data Sources.
Automatic Hierarchical Color Image Classification
Department of Computer Science, Cornell University, Ithaca, NY 14853, USA
EURASIP Journal on Advances in Signal Processing 2003, 2003:453751 doi:10.1155/S1110865703211161
The electronic version of this article is the complete one and can be found online at: http://asp.eurasipjournals.com/content/2003/2/453751
Received: | 20 March 2002 |
Revisions received: | 6 November 2002 |
Published: | 25 February 2003 |
© 2003 Copyright © 2003 Hindawi Publishing Corporation
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