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- W2054846838 abstract "This paper proposes an efficient approach for unsupervised segmentation of salient objects from color images. A set ofGaussian models are first estimated based on a pre-segmentation result of the input image, and then for each pixel, a setof normalized color likelihood measures to each Gaussian model are calculated. The color saliency and spatial saliencyof Gaussian models are exploited to generate the pixel-wise saliency map. By thresholding the saliency map, the pixelsare 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 geodesicdistances to salient object and background are calculated based on the object likelihood map. By comparing the twogeodesic distances, uncertain pixels are finally classified into salient object or background. Experimental resultsdemonstrate the better segmentation performance of the proposed approach." @default.
- W2054846838 created "2016-06-24" @default.
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- W2054846838 date "2010-07-11" @default.
- W2054846838 modified "2023-09-23" @default.
- W2054846838 title "Unsupervised salient object segmentation from color images" @default.
- W2054846838 doi "https://doi.org/10.1117/12.863360" @default.
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