Robust t-distribution mixture modeling via spatially directional information

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.