A Mixture Model for Image Boundary Detection Fusion

Yinghui ZHANG  Hongjun WANG  Hengxue ZHOU  Ping DENG  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E101-D   No.4   pp.1159-1166
Publication Date: 2018/04/01
Publicized: 2018/01/18
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2017EDP7314
Type of Manuscript: PAPER
Category: Image Processing and Video Processing
Keyword: 
expectation maximization,  image boundary detection fusion,  image segmentation,  mixture model,  

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Summary: 
Image boundary detection or image segmentation is an important step in image analysis. However, choosing appropriate parameters for boundary detection algorithms is necessary to achieve good boundary detection results. Image boundary detection fusion with unsupervised parameters can output a final consensus boundary, which is generally better than using unsupervised or supervised image boundary detection algorithms. In this study, we theoretically examine why image boundary detection fusion can work well and we propose a mixture model for image boundary detection fusion (MMIBDF) to achieve good consensus segmentation in an unsupervised manner. All of the segmentation algorithms are treated as new features and the segmentation results obtained by the algorithms are the values of the new features. The MMIBDF is designed to sample the boundary according to a discrete distribution. We present an inference method for MMIBDF and describe the corresponding algorithm in detail. Extensive empirical results demonstrate that MMIBDF significantly outperforms other image boundary detection fusion algorithms and the base image boundary detection algorithms according to most performance indices.