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In this study, the authors introduce a new weighted Student's t-mixture model (WSMM) for image segmentation. Gaussian distribution and Student's t-distribution are the two commonly used probabilities in the finite mixture model (FMM). The Student's t-mixture model has come to be regarded as an alternative to Gaussian mixture models, as it is heavily tailed and more robust for outliers. Moreover, the pixels are considered independent of each other in the FMM. Although some existing methods incorporate the spatial relationship between neighbouring pixels, they do not consider the relationship between spatial information and clustering information, thus those reported methods remain sensitive to noise. The advantages of the authors method are as follows: first, the authors introduce WSMM to incorporate the local spatial information, pixel intensity value and clustering information in an image. Second, the authors model is simple, easy to implement and has a good balance between noise insensitiveness and image detail preservation. Third, they adopt the gradient method and expectation maximisation algorithm, which allow for simultaneous estimation of optimal parameters. Finally, the most useful statistical tool for image segmentation, the well-known hidden Markov random field model, is a special case of their model. Thus, their method is general enough for model-based techniques construction. Experimental results on synthetic and real images demonstrate the improved robustness and effectiveness of their approach.
Date of Publication: April 2013