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Bottom-up saliency detection techniques extract salient regions in an image while free-viewing the image. We have approached the problem with three different low-level cues- graph based rarity, spatial compactness and background prior. First, the image is broken into similar colored patches, called superpixels. To measure rarity we represent the image as a graph with superpixels as node and exponential...
Bottom-up saliency detection algorithms identify distinct regions in an image, with rare occurrence of local feature distributions. Notable among those works published recently, use local and global contrast, spectral analysis of the entire image or graph based feature mapping. Whereas, we propose a novel unsupervised method using color compactness and statistical modeling of the background cues,...
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