Unsupervised Salient Object Matting

In this paper, we present a new, easy-to-generate method that is capable of precisely matting salient objects in a large-scale image set in an unsupervised way. Our method extracts only salient object without any user-specified constraints or a manual-thresholding of the saliency-map, which are essentially required in the image matting or saliency-map based segmentation, respectively. In order to provide a more balanced visual saliency as a response to both local features and global contrast, we propose a new, coupled saliency-map based on a linearly combined conspicuity map. Also, we introduce an adaptive tri-map as a refined segmented image of the coupled saliency-map for a more precise object extraction. The proposed method improves the segmentation performance, compared to image matting based on two existing saliency detection measures. Numerical experiments and visual comparisons with large-scale real image set confirm the useful behavior of the proposed method.