Quantitative Analysis of Human-Model Agreement in Visual Saliency Modeling: A Comparative Study

Visual attention is a process that enables biological and machine vision systems to select the most relevant regions from a scene. Relevance is determined by two components: 1) top-down factors driven by task and 2) bottom-up factors that highlight image regions that are different from their surroun...

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Bibliographic Details
Published in:	IEEE transactions on image processing Vol. 22; no. 1; pp. 55 - 69
Main Authors:	Borji, Ali, Sihite, D. N., Itti, L.
Format:	Journal Article
Language:	English
Published:	New York, NY IEEE 01.01.2013 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Analytical models Applied sciences Area Under Curve Arrays Artificial intelligence Attention - physiology Bottom-up attention Computational Biology Computational modeling Computer science; control theory; systems Databases, Factual Detection, estimation, filtering, equalization, prediction Exact sciences and technology eye movement prediction Eye movements Eye Movements - physiology Government Humans Image processing Image Processing, Computer-Assisted - methods Information, signal and communications theory Mathematical models model comparison Models, Statistical Organizing Pattern recognition Pattern recognition. Digital image processing. Computational geometry Photic Stimulation Predictive models Searching Signal and communications theory Signal processing Signal, noise State of the art Studies Telecommunications and information theory Videos Vision systems Visual visual attention visual saliency Visualization Computer vision State of the art Target tracking model comparison Artificial vision Vision system Pattern recognition Object recognition Computational complexity Modeling Eye movement Robotics Accuracy eye movement prediction Bottom-up attention Quality control Object detection Open market Visual saliency Visual attention Target detection Comparative study Quantitative analysis Image evaluation
ISSN:	1057-7149, 1941-0042, 1941-0042
Online Access:	Get full text
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