Salient object detection via color contrast and color distribution
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Visual saliency detection using information divergence
Pattern Recognition
Optimal contrast based saliency detection
Pattern Recognition Letters
Object recognition based on the region of interest and optical bag of words model
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Visual Saliency with Statistical Priors
International Journal of Computer Vision
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We introduce a saliency model based on two key ideas. The first one is considering local and global image patch rarities as two complementary processes. The second one is based on our observation that for different images, one of the RGB and Lab color spaces outperforms the other in saliency detection. We propose a framework that measures patch rarities in each color space and combines them in a final map. For each color channel, first, the input image is partitioned into non-overlapping patches and then each patch is represented by a vector of coefficients that linearly reconstruct it from a learned dictionary of patches from natural scenes. Next, two measures of saliency (Local and Global) are calculated and fused to indicate saliency of each patch. Local saliency is distinctiveness of a patch from its surrounding patches. Global saliency is the inverse of a patch's probability of happening over the entire image. The final saliency map is built by normalizing and fusing local and global saliency maps of all channels from both color systems. Extensive evaluation over four benchmark eye-tracking datasets shows the significant advantage of our approach over 10 state-of-the-art saliency models.