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 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 eye fixation data (static scenes [5] and dynamic scenes [16] ) and some psychological patterns.
In REF , Seo and Milanfar proposed a space-time saliency detection method which is based on a bottom-up framework and uses local regression kernels from a video as local features which differs from conventional filter responses.
Static and Space-time Visual Saliency Detection by Self-Resemblance
Journal of vision
Journal of vision
Psychology Medicine