Static and space-time visual saliency detection by self-resemblance.
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 (N. Bruce & J. Tsotsos, 2006) and dynamic scenes (L. Itti & P. Baldi, 2006)) and some psychological patterns.
Seo
Hae Jong
HJ
Electrical Engineering Department, University of California, Santa Cruz, Santa Cruz, CA, USA. rokaf@soe.ucsc.edu
Milanfar
Peyman
P
D000465:Algorithms; D001288:Attention; D003198:Computer Simulation; D005403:Fixation, Ocular; D006801:Humans; D008960:Models, Psychological; D009038:Motion; D009039:Motion Perception; D010775:Photic Stimulation / Q000379:methods; D012680:Sensitivity and Specificity; D013028:Space Perception; D013995:Time; D013998:Time Perception; D014785:Vision, Ocular