A Novel Approach to Object/Background Segmentation Based on the Probabilistic Graphical Model

Graph cut as a powerful optimization technique for minimizing MRF (Markov Random Field) [12] energy functions has been successfully applied to image segmentation. In this paper, we adopt an MRF model for object/background segmentation. The theoretical framework is based on maximum a posterior estimation via the graph-cut energy optimization method. Parameters are estimated with a novel parameter estimation algorithm. The novel parameter estimation algorithm is a variant of the expectation maximization (EM) algorithm [1, 5] with prior influence factors. Characteristic features related to the information in color, texture and position are extracted for each pixel. Experimental results demonstrate the effectiveness of our approach.

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