Unsupervised salient object segmentation from color images 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 presegmentation 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 pixelwise 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 objectbackground 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.

Salient Object Detection based on Multiple Priors Fusion Salient object detection can be utilized to detect the most significant regions in various environments, which has been regarded as foundation of computer vision. Different saliency models use different prior or knowledge. We propose a multi priors fusion method for saliency measure, which integrates background prior with foreground prior and center prior. Firstly, through each boundary of the image, we can get four saliency maps, and fuse them to get the background prior saliency map. Secondly, we utilize boundary extension method to highlight regions, and these regions can be regarded as the queries of manifold ranking for the foreground prior saliency map. Thirdly, the corners on the image are obtained, filtered by the foreground region, and then clustered into a point as the center of Gaussian model, which is used to calculate the center prior saliency map. Finally, the above three kinds of priorbased saliency maps are fused via the proposed fusion framework to gain a better final saliency map. Compared with fifteen methods, the experimental results on ECSSD and MSRA10K show that our proposed method achieves better saliency detection results.