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HSUAN-YING CHEN AND JIN-JANG LEOU
Department of Computer Science and Information Engineering
National Chung Cheng University
Chiayi, 621 Taiwan
Human perception tends to firstly pick attended regions, which correspond to
prominent objects in an image. Visual attention region detection simulates the behavior
of the human visual system (HVS) and detects regions of interest (ROIs) in the image. In
this study, a visual attention region detection approach using low-level texture and object
features is proposed. The new and improved (shifted) functions are proposed and used in
both the proposed texture and object features to ensure that all attended pixels will be
extracted. The proposed approach can generate high-quality spatial saliency maps in an
effective manner. As compared with three existing approaches, including Stentiford¡¦s,
Zhai/ Shah¡¦s, and Park/Moon¡¦s approaches, the proposed approach has a better performance
of extracting ROIs in images and low computational complexity.
Received September 30, 2008; revised February 4, 2009; accepted April 16, 2009.
Received September 22, 2008; revised December 30, 2008 & April 21, 2009; accepted April 30, 2009.
Communicated by Tong-Yee Lee.
* This work was supported in part by the National Science Council of Taiwan, R.O.C. under Grants No. NSC
95-2221-E-194-020-MY3 and NSC 96-2221-E-194-033-MY3.