Isomorphic Manifold Inference for hair segmentation
Hair segmentation is challenging due to the diverse appearance, irregular region boundary and the influence of complex background. To deal with this problem, we propose a novel method, named Isomorphic Manifold Inference (IMI). Given a head-shoulder image, a Coarse Hair Probability Map (Coarse HPM), each element of which represents the probability of the pixel being hair, is initially calculated by exploring hair location and color priors. Then, based on an observation that similar Coarse HPMs imply similar segmentations, we formulate Coarse HPM and corresponding ground segmentation (Optimal HPM) as a pair of isomorphic manifolds. Under this formulation, final hair segmentation is inferred from the Coarse HPM with regression techniques. In this way, the IMI implicitly exploits the hair-specific prior embodied in the training set. Extensive experimental comparisons are conducted and the results strongly encourage the method. The generality of IMI to other class-specific image segmentation is also discussed.