Bayesian image segmentation fusion

• Impact Factor:8.139
• DOI number:10.1016/j.knosys.2014.07.021
• Affiliation of Author(s):西南交通大学
• Journal:Knowledge-Based Systems
• Place of Publication:NETHERLANDS
• Key Words:Bayesian modelImage segmentation fusionVariational inferenceGeneration modelExpectation maximization
• Abstract:Image segmentation fusion can output a final consensus segmentation which in general is better than those of unsupervised image segmentation algorithms. In this paper, the image segmentation fusion is firstly formalized as a combinatorial optimization problem in terms of information theory. Then a Bayesian image segmentation fusion (BISF) model is proposed for a good consensus segmentation. We treat all the segmentation algorithms (or the same algorithm with different parameters) as new features and the segmentations of algorithms as values of the new features, which simplifies image segmentation fusion problems in computation complexity. Based on this idea, a generative model BISF is designed to sample the segmentation according to the discrete distribution, and the inference for BISF and the corresponding algorithm are illustrated in detail. At last, extensive empirical results demonstrate that BISF significantly outperforms other image segmentation fusion algorithms and the popular image segmentation algorithms or algorithms with different parameters in terms of popular indices.
• Co-author:Yinghui Zhang,RuihuaNie,YanYang,BoPeng,TianruiLi
• First Author:Hongjun Wang
• Indexed by:Academic papers
• Correspondence Author:HongjunWang
• Document Code:20144300128196
• Discipline:Engineering
• First-Level Discipline:Computer Science and Technology
• Volume:Volume 71
• Page Number:162-168
• ISSN No.:0950-7051
• Translation or Not:no
• Date of Publication:2014-08-19