Unsupervised salient object segmentation from color images

Abstract

This paper proposes an efficient approach for unsupervised segmentation of salient objects from color images. A set of Gaussian models are first estimated based on a pre-segmentation result of the input image, and then for each pixel, a set of normalized color likelihood measures to each Gaussian model are calculated. The color saliency and spatial saliency of Gaussian models are exploited to generate the pixel-wise saliency map. By thresholding the saliency map, the pixels are classified into object seed pixels, background seed pixels and uncertain pixels to obtain the trimap. For each pixel, the probability belonging to salient object/background is evaluated using kernel density estimation, and the geodesic distances to salient object and background are calculated based on the object likelihood map. By comparing the two geodesic distances, uncertain pixels are finally classified into salient object or background. Experimental results demonstrate the better segmentation performance of the proposed approach.