Learning optimal filter representation for texture classification

Zhang Peng, Peng Jing, Bill Buckles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Crucial to texture classification are texture features and classifiers that operate on the features. There are several approaches to computing texture features. Of particular interest is multichannel filtering because of its simplicity. Multichannel filtering works by decomposing the frequency domain of an image, resulting in a bank of filtered feature images. Many techniques have been proposed to optimize multichannel filtering. However, the optimization is with respect to image representation, thus giving no guarantee for texture classification. This paper proposes a novel technique for learning optimal filters for texture classification. We use regularization techniques such as support vector machines (SVMs) to learn multichannel filters. Since filter training in our approach is naturally tied to classifier training, the resulting filters are optimized for classification. Experimental results validate the efficacy of our proposed technique.

Original languageEnglish
Title of host publicationProceedings - 18th International Conference on Pattern Recognition, ICPR 2006
Pages1138-1141
Number of pages4
DOIs
StatePublished - 1 Dec 2006
Event18th International Conference on Pattern Recognition, ICPR 2006 - Hong Kong, China
Duration: 20 Aug 200624 Aug 2006

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume2
ISSN (Print)1051-4651

Other

Other18th International Conference on Pattern Recognition, ICPR 2006
CountryChina
CityHong Kong
Period20/08/0624/08/06

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  • Cite this

    Peng, Z., Jing, P., & Buckles, B. (2006). Learning optimal filter representation for texture classification. In Proceedings - 18th International Conference on Pattern Recognition, ICPR 2006 (pp. 1138-1141). [1699410] (Proceedings - International Conference on Pattern Recognition; Vol. 2). https://doi.org/10.1109/ICPR.2006.753