Learning saliency-based visual attention: A review

被引：73

作者：

Zhao, Qi ^{[1
]}

Koch, Christof ^{[2
,3
]}

机构：

[1] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 117548, Singapore

[2] CALTECH, Pasadena, CA 91125 USA

[3] Allen Inst Brain Sci, Seattle, WA USA

来源：

SIGNAL PROCESSING | 2013年 / 93卷 / 06期

基金：

美国国家科学基金会;

关键词：

Visual attention; Machine learning; Feature representation; Central fixation bias; Public eye tracking datasets; EYE-MOVEMENTS; FIXATION SELECTION; SCENE; CONTRAST; ALGORITHMS; MODEL; COLOR; STATISTICS; ALLOCATION; POSITION;

D O I：

10.1016/j.sigpro.2012.06.014

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Humans and other primates shift their gaze to allocate processing resources to a subset of the visual input. Understanding and emulating the way that human observers free-view a natural scene has both scientific and economic impact. It has therefore attracted the attention from researchers in a wide range of science and engineering disciplines. With the ever increasing computational power, machine learning has become a popular tool to mine human data in the exploration of how people direct their gaze when inspecting a visual scene. This paper reviews recent advances in learning saliency-based visual attention and discusses several key issues in this topic. (c) 2012 Elsevier B.V. All rights reserved.

引用

页码：1401 / 1407

页数：7

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