A detail-preserving mixture model for image segmentation
This paper presents a new finite mixture model for image segmentation. First, in order to take into account the spatial dependencies in an image, existing mixture models use a constant temperature parameter (β) throughout the image for every label. The constant value of β reduces the impact of noise in homogeneous regions but negatively affects segmentation along the border of two regions. We propose a new way to use a different value of β throughout the image. Secondly, in order to incorporate the correlation between each centre pixel and its neighboring pixels, existing mixture model gives the same importance to all pixels in a neighborhood window. We assign different weights to different pixels appearing in the window, which is based on the fact that the clique strength should be reduced with distance. Thirdly, our model is based on the Student's-t distribution, which is heavily tailed and more robust than Gaussian. We exploit Dirichlet distribution and Dirichlet law to incorporate the spatial relationships between pixels in an image. Finally, expectation maximization (EM) algorithm is adopted to maximize the data log-likelihood and to optimize the parameters. The performance is compared to other existing models based on the model-based techniques, demonstrating superiority of the proposed model for image segmentation.