Robust t-distribution mixture modeling via spatially directional information
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Finite mixture model (FMM) has been successfully applied to many practical applications in recent years. However, a significant shortcoming of the FMM with Gaussian distribution is that it is sensitive to noise. Recently, Student’s t-distribution with a heavier-tailed acting as a robust alternative to Gaussian distribution is getting more and more attentions. In this paper, we propose a new Student’s t-distribution finite mixture model which incorporates the spatial relationships between the pixels and simultaneously imposes spatial smoothness. In addition, the pixel’s neighbor directional information is also integrated into the proposed model. Furthermore, the pixels’ label probability proportions are explicitly represented as probability vectors to reduce the computational costs of the proposed model. We use the gradient descend method to estimate the unknown parameters of the proposed model. Comprehensive experiments are conducted on both synthetic and natural grayscale images. The experimental results demonstrate the superiority of the proposed model over some existing models.
KeywordsFinite mixture model Student’s t-distribution Spatially directional information Image segmentation Gradient descent
The authors would like to thank the anonymous reviewers for their valuable comments and suggestions, which greatly helped to improve 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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