Nonparametric Bottom-Up Saliency Detection by Self-Resemblance

We present a novel bottom-up saliency detection algorithm. Our method computes so-called local regression kernels (i.e., local features) from the given image, which measure the likeness of a pixel to its surroundings. Visual saliency is then computed using the said "self-resemblance" measure. The frame work results in a saliency map where each pixel indicates the statistical likelihood of saliency of a feature matrix given its surrounding feature matrices. As a similarity measure, matrix cosine similarity, (a generalization of cosine similarity) is employed. State of the art performance is demonstrated on commonly used human eve fixation data [3] and some psychological patterns.