Category-specific object segmentation via unsupervised discriminant shape. (April 2017) - Dai, Lingzheng; Yang, Jian; Chen, Liang; Li, Junxia
Abstract: Category-specific object segmentation has been a long-standing research topic in pattern recognition. This paper presents an unsupervised discriminant shape (UDS) to address category-specific object segmentation by incorporating the proposed shape prior into an intuitive energy minimization framework. Recently, based on the region proposal methods, deep Convolutional Neural Networks (CNNs) provide access to candidate segments in categories of interest from images. However, the segments obtained from bottom-up proposals tend to undershoot or overshoot objects and are easily classified into one specific class. To address this problem, we propose an unsupervised discriminant projection based clustering algorithm (UDC) to obtain more precise shape prior to guide the segmentation, and the class-specific proposals are clustered based on their projections onto the discriminant projection direction. Based on the set of proposals, we then obtain the prior information of foreground UDS with an easy voting scheme. The derived UDS prior is finally utilized in the subsequent energy minimizing formulation based figure-ground segmentation. We conduct extensive and comprehensive evaluations on the MSRC, Object Discovery, Fashionista and PASCAL-S datasets, demonstrating the effectiveness and robustness of the UDS based segmentation. Abstract : Highlights: A novel unsupervised discriminant shape is proposed for segmenting images. An unsupervised discriminant clustering method is built for precise shape prior. Our model can produce reliable segmentation for category-specific objects. Evaluations on 4 datasets demonstrate the effectiveness of our method.