Hair style retrieval by semantic mapping on informative patches

Hair is an important aspect of human appearance. Hair color has been employed to facilitate face retrieval in literature, but hair style is still dismissed because of the challenges of its segmentation. In this paper, we propose a novel hair style retrieval algorithm in unconstrained environments. In contrary to defining similarity based on features, we base our measurement directly on hair shapes. To bridge the “semantic gap” between low-level features and high-level hairstyle, we incorporate mapping function which integrates local and pairwise evidences in MRF framework. Additionally, we propose a RankBoost learning algorithm to select the most informative patches integrating the heuristic information of mapping function accuracy. Our method is applied to the “Labeled Faces in the Wild” dataset and yields promising results.