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Proceedings Paper

A Comparison of Matrix Texture Features Using a Maximum Likelihood Texture Classifier
Author(s): Jon R. Berry; John Goutsias
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Paper Abstract

The performance of various matrix features in classifying synthetic and natural textures is compared by using the features directly in a maximum likelihood texture classifier (MLTC). The matrix texture features under consideration include the spatial gray level dependence matrix (SGLDM), the neighboring gray level dependence matrix (NGLDM) and the neighboring spatial gray level dependence matrix (NSGLDM). By adopting the MLTC we avoid the various problems associated with the use of scalar features extracted from the matrices under consideration, while we obtain excellent classification results.

Paper Details

Date Published: 1 November 1989
PDF: 12 pages
Proc. SPIE 1199, Visual Communications and Image Processing IV, (1 November 1989); doi: 10.1117/12.970043
Show Author Affiliations
Jon R. Berry, The Johns Hopkins University (United States)
John Goutsias, The Johns Hopkins University (United States)


Published in SPIE Proceedings Vol. 1199:
Visual Communications and Image Processing IV
William A. Pearlman, Editor(s)

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