Open Access

Automatic Hierarchical Color Image Classification

EURASIP Journal on Advances in Signal Processing20032003:453751

https://doi.org/10.1155/S1110865703211161

Received: 20 March 2002

Published: 25 February 2003

Abstract

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.

Keywords

image classification color correlogram classification tree

Authors’ Affiliations

(1)
Department of Computer Science, Cornell University

Copyright

© Copyright © 2003 Hindawi Publishing Corporation 2003