Texture Segmentation Comparison Using Grey Level Co-Occurrence Probabilities and Markov Random Fields

Authors:
David A. Clausi;Bing Yue
Affiliations:
University of Waterloo, Canada;University of Waterloo, Canada
Venue:
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Year:
2004

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Data & Knowledge Engineering

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Abstract

The discrimination ability of texture features derived from Gaussian Markov random fields (GMRFs) and grey level co-occurrence probabilities (GLCPs) are compared and contrasted. More specifically, the role of window size in feature consistency and separability as well as the role of multiple textures within a window are investigated. GLCPs are demonstrated to have improved discrimination ability relative to MRFs with decreasing window size, an important concept when performing image segmentation. On the other hand, GLCPs are more sensitive to texture boundary confusion than GMRFs.