RT Journal Article
A1 Seo, Hae Jong
A1 Milanfar, Peyman
T1 Static and space-time visual saliency detection by self-resemblance
JF Journal of Vision
JO JOV
YR 2009
DO 10.1167/9.12.15
JF Journal of Vision
VO 9
IS 12
SP 15
OP 15
SN 1534-7362
AB  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.
RD 12/11/2019
UL https://doi.org/10.1167/9.12.15