Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1867
Title: Adaptive segmentation of textured images by using the coupled Markov random field model
Authors: Xia, Yong
Feng, D. David
Zhao, Rongchun
Subjects: Image segmentation
Image texture analysis
Random field
Simulated annealing
Issue Date: Nov-2006
Publisher: IEEE
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.
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Type: Journal/Magazine Article
URI: http://hdl.handle.net/10397/1867
DOI: 10.1109/TIP.2006.877513
ISSN: 1057-7149
Appears in Collections:EIE Journal/Magazine Articles

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