A variable weight image segmentation algorithm based on Markov Random Field

作者：Li, Yibing; Yang, Peng; Ye, Fang^*

来源：Journal of Information and Computational Science, 2014, 11(3): 717-723.

DOI：10.12733/jics20102789

• Conditional models
• Image segmentation algorithm
• Markov Random Field model
• Markov Random Fields
• Optimal segmentation
• Potential function
• Variable weight
• Variable weight methods

Proposed a weighted image segmentation method based on Markov Random Field and context information of the image. On the basis of traditional potential function, the paper introduces the relationship of neighborhood pixels to describe the probability of pixels being partitioned into the same class. We convert image segmentation problem into maximum a posterior for extreme value by Bayes theory. Meanwhile, we introduce the variable weight method to connect feature field and label field. Finally, the iterative conditional model is applied to acquire optimal segmentations. Compared to K-means and traditional MRF algorithm, the proposed method presents effectiveness and robustness in the experiments.

• 出版日期2014
• 单位哈尔滨工程大学