A Markov random field based hybrid algorithm with simulated annealing and genetic algorithm for image segmentation

被引：0

作者：

Du, Xinyu ^{[1
]}

Li, Yongjie ^{[1
]}

Chen, Wufan ^{[1
]}

Zhang, Yi ^{[1
]}

Yao, Dezhong ^{[1
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Life Sci & Technol, Ctr Neuroinformat, Chengdu 610054, Peoples R China

来源：

ADVANCES IN NATURAL COMPUTATION, PT 1 | 2006年 / 4221卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, a simulated algorithm-genetic (SA-GA) hybrid algorithm based on a Markov Random Field (MRF) model (MRF-SA-GA) is introduced for image de-noising and segmentation. In this algorithm, a population of potential solutions is maintained at every generation, and for each solution a fitness value is calculated with a fitness function, which is constructed based on the MRF potential function according to Metropolis algorithm and Bayesian rule. Two experiments are selected to verify the performance of the hybrid algorithm, and the preliminary results show that MRF-SA-GA outperforms SA and GA alone.

引用

页码：706 / 715

页数：10

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