Previous [ 1] [ 2] [ 3] [ 4] [ 5] [ 6] [ 7] [ 8] [ 9] [ 10] [ 11] [ 12] [ 13] [ 14] [ 15] [ 16] [ 17] [ 18] [ 19] [ 20] [ 21] [ 22] [ 23]

¡@

Journal of Information Science and Engineering, Vol. 26 No. 5, pp. 1657-1675 (September 2010)

Visual Attention Region Detection Using Texture and Object Features*

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.

Keywords: visual attention, saliency map, regions of interest (ROIs), feature extraction, multi-scaled model

Full Text (¥ş¤åÀÉ) Retrieve PDF document (201009_06.pdf)

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.