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Proceedings Paper

Wavelet-based fractal signature for texture classification
Author(s): Fausto Espinal; Rajesh Chandran
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Paper Abstract

Efficient feature extraction metrics are crucial in many computer vision applications. One such application is texture classification which involves classifying samples as members of one of a preset number of classes. These classes are chosen to correspond with our human intuition of which textures are different from others. In this work we use the wavelet-based fractal signature, a new multichannel texture model introduced previously which characterizes patterns as 2D functions in a Besov space. The wavelet-based fractal signature generates an n-dimensional surface, which is then used for classification by a fuzzy self-organizing feature map as well as two other supervised classification techniques. The feature space has a low dimensionality and as a result is classified in few training epochs. Experimental results are presented for a test set of textures of different types.

Paper Details

Date Published: 26 March 1998
PDF: 10 pages
Proc. SPIE 3391, Wavelet Applications V, (26 March 1998); doi: 10.1117/12.304910
Show Author Affiliations
Fausto Espinal, Univ. of South Carolina (United States)
Rajesh Chandran, Univ. of South Carolina (United States)

Published in SPIE Proceedings Vol. 3391:
Wavelet Applications V
Harold H. Szu, Editor(s)

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