Scene Parsing From an MAP Perspective

Scene parsing is an important problem in the field of computer vision. Though many existing scene parsing approaches have obtained encouraging results, they fail to overcome within-category inconsistency and intercategory similarity of superpixels. To reduce the aforementioned problem, a novel method is proposed in this paper. The proposed approach consists of three main steps: 1) posterior category probability density function (PDF) is learned by an efficient low-rank representation classifier (LRRC); 2) prior contextual constraint PDF on the map of pixel categories is learned by Markov random fields; and 3) final parsing results are yielded up to the maximum a posterior process based on the two learned PDFs. In this case, the nature of being both dense for within-category affinities and almost zeros for intercategory affinities is integrated into our approach by using LRRC to model the posterior category PDF. Meanwhile, the contextual priori generated by modeling the prior contextual constraint PDF helps to promote the performance of scene parsing. Experiments on benchmark datasets show that the proposed approach outperforms the state-of-the-art approaches for scene parsing.