A comparative study on the K-views classifier and Markov random fields for image texture classification

Authors:
Minh Pham;Mei Xiang;Chih-Cheng Hung;Bor-Chen Kuo
Affiliations:
Southern Polytechnic State University, Marietta, GA;Southern Polytechnic State University, Marietta, GA;Southern Polytechnic State University, Marietta, GA;National Taichung Teachers College, Taichung, Taiwan, R. O. C.
Venue:
Proceedings of the 43rd annual Southeast regional conference - Volume 1
Year:
2005

Citing 2
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Image texture classification using datagrams and characteristic views

Proceedings of the 2003 ACM symposium on Applied computing
Multiresolution Gauss-Markov random field models for texture segmentation

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Abstract

We compared two image classifiers which incorporate contextual information to classify each pixel in the raw images in this study: namely, the K-views classifier and the classifier using Markov Random Fields (MRF). These procedures incorporate contextual information by using spatial features. Preliminary experimental results are provided in this report.