Edge preserving spatially varying mixtures for image segmentation

A new hierarchical Bayesian model is proposed for image segmentation based on Gaussian mixture models (GMM) with a prior enforcing spatial smoothness. According to this prior, the local differences of the contextual mixing proportions (i.e. the probabilities of class labels) are Student's t-distributed. The generative properties of the Student's t-pdf allow this prior to impose smoothness and simultaneously model the edges between the segments of the image. A maximum a posteriori (MAP) expectationmaximization (EM) based algorithm is used for Bayesian inference. An important feature of this algorithm is that all the parameters are automatically estimated from the data in closed form. Numerical experiments are presented that demonstrate the superiority of the proposed model for image segmentation as compared to standard GMM-based approaches and to GMM segmentation techniques with "standard" spatial smoothness constraints.

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Giorgos Sfikas, Christophoros Nikou, Nikolas P. Galatsanos
• Keywords: Computer Vision, Contextual Mixing Proportions, CVPR 2008, Hierarchical Bayesian Model, Image Segmentation, Segmentation Techniques, Spatial Smoothness Constraints