|Title||Texture segmentation comparison using grey level co-occurrence probabilities and Markov random fields|
|Publication Type||Conference Paper|
|Year of Publication||2004|
|Authors||Clausi, D. A., and B. Yue|
|Conference Name||17th International Conference on Pattern Recognition (ICPR)|
|Conference Location||Cambridge, United Kingdom|
|Keywords||discrimination ability, Feature Extraction, Gaussian Markov random fields, Gaussian processes, grey level co-occurrence probabilities, image segmentation, image texture, Markov processes, probability, texture features, texture segmentation|
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.