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Grayscale image segmentation by spatially variant mixture model with student’s t-distribution

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Abstract

A spatially variant finite mixture model with Student’s t-distribution component function is proposed for grayscale image segmentation. This model employs a new weight function which contains the information along the different spatial directions indicating the relationship of the pixels in the neighborhood. The label probability proportions are explicitly represented as probability vectors in the model. Gradient descend method is used to update the unknown parameters. The proposed model contains fewer parameters and it is easy to be implemented compare with the Markov random field (MRF) models. Comprehensive experiments on synthetic and natural images are carried out to demonstrate that the proposed model outperforms some other related ones.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable comments and suggestions, which greatly helped to improve both the technical content and the presentation quality of the paper. This work was supported by National Basic Research Program of China (973 Program) under Grant 2011CB302201, and National Nature Science Foundation of China under grant No. 60931160441 and No. 61003042.

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Authors and Affiliations

  1. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, People’s Republic of China

    Taisong Xiong

  2. Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, 610065, People’s Republic of China

    Zhang Yi & Lei Zhang

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  1. Taisong Xiong
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  2. Zhang Yi
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  3. Lei Zhang
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Correspondence to Zhang Yi.

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Xiong, T., Yi, Z. & Zhang, L. Grayscale image segmentation by spatially variant mixture model with student’s t-distribution. Multimed Tools Appl 72, 167–189 (2014). https://doi.org/10.1007/s11042-012-1336-1

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  • DOI: https://doi.org/10.1007/s11042-012-1336-1

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