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http://dx.doi.org/10.5573/ieie.2014.51.7.157

An Artificial Visual Attention Model based on Opponent Process Theory for Salient Region Segmentation  

Jeong, Kiseon (Department of Electronic Engineering, Chonbuk National University)
Hong, Changpyo (Department of Electronic Engineering, Chonbuk National University)
Park, Dong Sun (Department of Electronic Engineering, Chonbuk National University)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.51, no.7, 2014 , pp. 157-168 More about this Journal
Abstract
We propose an novel artificial visual attention model that is capable of automatic detection and segmentation of saliency region on natural images in this paper. The proposed model is based on human visual perceptions in biological vision and contains there are main contributions. Firstly, we propose a novel framework of artificial visual attention model based on the opponent process theory using intensity and color features, and an entropy filter is designed to perceive salient regions considering the amount of information from intensity and color feature channels. The entropy filter is able to detect and segment salient regions in high segmentation accuracy and precision. Lastly, we also propose an adaptive combination method to generate a final saliency map. This method estimates scores about intensity and color conspicuous maps from each perception model and combines the conspicuous maps with weight derived from scores. In evaluation of saliency map by ROC analysis, the AUC of proposed model as 0.9256 approximately improved 15% whereas the AUC of previous state-of-the-art models as 0.7824. And in evaluation of salient region segmentation, the F-beta of proposed model as 0.7325 approximately improved 22% whereas the F-beta of previous state-of-the-art models.
Keywords
entropy filter; salient region detection; salient region segmentation; opponent process theory;
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