Improving semantic texton forests with a Markov random field for image segmentation

Abstract

Semantic image segmentation is a major and challenging problem in computer vision, which has been widely researched over decades. Recent approaches attempt to exploit contextual information at different levels to improve the segmentation results. In this paper, we propose a new approach for combining semantic texton forests (STFs) and Markov random fields (MRFs) for improving segmentation. STFs allow fast computing of texton codebooks for powerful low-level image feature description. MRFs, with the most effective algorithm in message passing for training, will smooth out the segmentation results of STFs using pairwise coherent information between neighboring pixels. We evaluate the performance of the proposed method on two well-known benchmark datasets including the 21-class MSRC dataset and the VOC 2007 dataset. The experimental results show that our method impressively improved the segmentation results of STFs. Especially, our method successfully recognizes many challenging image regions that STFs failed to do.