Multiresolution superpixels for visual saliency detection

Salient regions are those that stand out from others in an image. We present an algorithm to detect salient regions in an image that is represented as superpixels at a number of resolutions. Superpixels are segments generated by oversegmenting an image and they form a perceptually meaningful representation that preserves the underlying image structure. The novelty of our method is the ranking of a superpixel by its dissimilarities with respect to other superpixels and highlighting the statistically salient region proportional to their rank. This is based on the premise that salient region group together and they stand out. We tested our method using standard data sets containing images of varied complexity and compared the results to ground truth data. Our results show that our saliency detection algorithm is robust to changes in color, object size, object location in image and background type.