Image segmentation using a hierarchical student's‐ t mixture model

Abstract

As a significant tool, finite mixture models (FMMs) have been widely used for image segmentation. However, there are two problems with standard FMMs: first, the conditional probability is sensitive to outliers. Second, the robustness to image noise is inadequate. In this study, the authors present a novel hierarchical Student's- t MM (HSMM), which includes standard FMMs as a sub-problem. Additionally, to incorporate more image spatial information, they apply a mean template not only to the prior/posterior probability, but also to the sub-conditional distribution. Thus, their HSMM is more robust to outliers and image noise owing to the spatial constraints from the mean template. In the standard SMM, a t -distribution is used to calculate the conditional probability. In this study, the authors present a novel hierarchical student's- t mixture model (HSMM), which includes the standard FMM as a sub-problem. Finally, though they use Student's- t -distribution to solve the image segment problems of this study, their HSMM achieves excellent performance, is elastic and can encompass any other model that is based on FMMs. Experimental results demonstrate that their proposed method is robust and effective.