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Hae Jong Seo, Peyman Milanfar; Static and space-time visual saliency detection by self-resemblance. Journal of Vision 2009;9(12):15. doi: 10.1167/9.12.15.
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© ARVO (1962-2015); The Authors (2016-present)
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.
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