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Proceedings Paper

Texture classification by a two-level hybrid scheme
Author(s): Gouchol Pok; Jyh-Charn S. Liu
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Paper Abstract

In this paper, we propose a novel feature extraction scheme for texture classification, in which the texture features are extracted by a two-level hybrid scheme, by integrating two statistical techniques of texture analysis. In the first step, the low level features are extracted by the Gabor filters, and they are encoded with the feature map indices, using Kohonen's SOFM algorithm. In the next step, the encoded feature images are processed by the Gabor filters, Gaussian Markov random fields (GMRF), and Grey level co- occurrence matrix (GLCM) methods to extract the high level features. By integrating two methods of texture analysis in a cascaded manner, we obtained the texture features which achieved a high accuracy for the classification of texture patterns. The proposed schemes were tested on the real microtextures, and the Gabor-GMRF scheme achieved 10 percent increase of the recognition rate, compared to the result obtained by the simple Gabor filtering.

Paper Details

Date Published: 17 December 1998
PDF: 9 pages
Proc. SPIE 3656, Storage and Retrieval for Image and Video Databases VII, (17 December 1998); doi: 10.1117/12.333882
Show Author Affiliations
Gouchol Pok, Texas A&M Univ. (United States)
Jyh-Charn S. Liu, Texas A&M Univ. (United States)


Published in SPIE Proceedings Vol. 3656:
Storage and Retrieval for Image and Video Databases VII
Minerva M. Yeung; Boon-Lock Yeo; Charles A. Bouman, Editor(s)

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