Shape-based image retrieval using support vector machines, Fourier descriptors and self-organizing maps

Document Type

Article

Publication Date

4-15-2007

Abstract

Image retrieval based on image content has become an important topic in the fields of image processing and computer vision. In this paper, we present a new method of shape-based image retrieval using support vector machines (SVM), Fourier descriptors and self-organizing maps. A list of predicted classes for an input shape is obtained using the SVM, ranked according to their estimated likelihood. The best match of the image to the top-ranked class is then chosen by the minimum mean square error. The nearest neighbors can be retrieved from the self-organizing map of the class. We employ three databases of 99, 216, and 1045 shapes for our experiment, and obtain prediction accuracy of 90%, 96.7%, and 84.2%, respectively. Our method outperforms some existing shape-based methods in terms of speed and accuracy. © 2006 Elsevier Inc. All rights reserved.

Identifier

33846643627 (Scopus)

Publication Title

Information Sciences

External Full Text Location

https://doi.org/10.1016/j.ins.2006.10.008

ISSN

00200255

First Page

1878

Last Page

1891

Issue

8

Volume

177

Grant

NSC 94-2213-E-216-024

Fund Ref

National Science Council

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