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Title: Adaptive segmentation of textured images by using the coupled Markov random field model
Authors: Xia, Y
Feng, DD
Zhao, R
Issue Date: Nov-2006
Source: IEEE transactions on image processing, Nov. 2006, v. 15, no. 11, p. 3559-3566
Abstract: Although simple and efficient, traditional feature-based texture segmentation methods usually suffer from the intrinsical less inaccuracy, which is mainly caused by the oversimplified assumption that each textured subimage used to estimate a feature is homogeneous. To solve this problem, an adaptive segmentation algorithm based on the coupled Markov random field (CMRF) model is proposed in this paper. The CMRF model has two mutually dependent components: one models the observed image to estimate features, and the other models the labeling to achieve segmentation. When calculating the feature of each pixel, the homogeneity of the subimage is ensured by using only the pixels currently labeled as the same pattern. With the acquired features, the labeling is obtained through solving a maximum a posteriori problem. In our adaptive approach, the feature set and the labeling are mutually dependent on each other, and therefore are alternately optimized by using a simulated annealing scheme. With the gradual improvement of features' accuracy, the labeling is able to locate the exact boundary of each texture pattern adaptively. The proposed algorithm is compared with a simple MRF model based method in segmentation of Brodatz texture mosaics and real scene images. The satisfying experimental results demonstrate that the proposed approach can differentiate textured images more accurately.
Keywords: Image segmentation
Image texture analysis
Random field
Simulated annealing
Publisher: Institute of Electrical and Electronics Engineers
Journal: IEEE transactions on image processing 
ISSN: 1057-7149
EISSN: 1941-0042
DOI: 10.1109/TIP.2006.877513
Rights: © 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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