Automatic texture feature selection for image pixel classification

doi:10.1016/j.patcog.2006.05.016

Automatic texture feature selection for image pixel classification

2006-11-01

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DOI：10.1016/j.patcog.2006.05.016

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Pixel-based texture classifiers and segmenters are typically based on the combination of texture feature extraction methods that belong to a single family (e.g., Gabor filters). However, combining texture methods from different families has proven to produce better classification results both quantitatively and qualitatively. Given a set of multiple texture feature extraction methods from different families, this paper presents a new texture feature selection scheme that automatically determines a reduced subset of methods whose integration produces classification results comparable to those obtained when all the available methods are integrated, but with a significantly lower computational cost. Experiments with both Brodatz and real outdoor images show that the proposed selection scheme is more advantageous than well-known general purpose feature selection algorithms applied to the same problem. (c) 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Keywords:

texture feature selection

supervised texture classification

multiple texture methods

multiple evaluation windows

Automatic texture feature selection for image pixel classification

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