A Compositional Exemplar-Based Model for Hair Segmentation

Hair is a very important part of human appearance. Robust and accurate hair segmentation is difficult because of challenging variation of hair color and shape. In this paper, we propose a novel Compositional Exemplar-based Model (CEM) for hair style segmentation. CEM generates an adaptive hair style (a probabilistic mask) for the input image automatically in the manner of Divide-and-Conquer, which can be divided into decomposition stage and composition stage naturally. For the decomposition stage, we learn a strong ranker based on a group of weak similarity functions emphasizing the Semantic Layout similarity (SLS) effectively; in the composition stage, we introduce the Neighbor Label Consistency (NLC) Constraint to reduce the ambiguity between data representation and semantic meaning and then recompose the hair style using alpha-expansion algorithm. Final segmentation result is obtained by Dual-Level Conditional Random Fields. Experiment results on face images from Labeled Faces in the Wild data set show its effectiveness.