DocumentCode :
789403
Title :
Adaptive Segmentation of Textured Images by Using the Coupled Markov Random Field Model
Author :
Xia, Yong ; Feng, Dagan ; Zhao, Rongchun
Author_Institution :
Sch. of Comput., Northwestern Polytech. Univ.
Volume :
15
Issue :
11
fYear :
2006
Firstpage :
3559
Lastpage :
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 :
Markov processes; image segmentation; image texture; maximum likelihood estimation; simulated annealing; Brodatz texture mosaics; adaptive segmentation; coupled Markov random field model; feature estimation; maximum a posteriori problem; simulated annealing; textured images; Computational modeling; Image segmentation; Image texture analysis; Information technology; Labeling; Layout; Markov random fields; Pixel; Signal processing algorithms; Simulated annealing; Image segmentation; image texture analysis; random field; simulated annealing;
fLanguage :
English
Journal_Title :
Image Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1057-7149
Type :
jour
DOI :
10.1109/TIP.2006.877513
Filename :
1709998
Link To Document :
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