Static and space-time visual saliency detection by self-resemblance

Abstract

We present a novel unified framework for both static and space -time saliency detection. Our method is a bottom-up approach and computes so-called local regression kernels (i.e., local descriptors) from the given image (or a video), which measure the likeness of a pixel (or voxel) to its surroundings. Visual saliency is then computed using the said "self-resemblance" measure. The framework results in a saliency map where each pixel (or voxel) indicates the statistical li kelihood 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 eye fixation data (static scenes (5) and dynamic scenes (16)) and some psychological patterns.