Wavelet-based fractal signature for texture classification

Fausto Espinal; Rajesh Chandran

doi:10.1117/12.304910

26 March 1998 Wavelet-based fractal signature for texture classification

Fausto Espinal, Rajesh Chandran

Proceedings Volume 3391, Wavelet Applications V; (1998) https://doi.org/10.1117/12.304910
Event: Aerospace/Defense Sensing and Controls, 1998, Orlando, FL, United States

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.

Citation Download Citation

Fausto Espinal and Rajesh Chandran "Wavelet-based fractal signature for texture classification", Proc. SPIE 3391, Wavelet Applications V, (26 March 1998); https://doi.org/10.1117/12.304910

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