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《Acta Electronica Sinica》 2013-06
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A Spectral Algorithm Based on Weighted Sparse Coding for Visual Saliency Detection

QIAN Xiao-liang,GUO Lei,HAN Jun-wei,CHENG Gong,YAO Xi-wen (School of Automation,Northwestern Polylechnical University,Xi'an,Shaanxi 710129,China)  
A spectral algorithm using weighted sparse coding is proposed for visual saliency detection in this paper.This algorithm is able to improve the accuracy of the traditional spectral saliency detection approaches while preserving their advantage of fast processing speed.Based on the traditional sparse coding algorithm,the sub-codes are weighted according to their incremental coding length.Then,the image is encoded by weighted sparse coding instead of directly transforming the raw images into frequency domain.Finally,we improve the multi-channel method developed in a recently published algorithm called image signature through information theory.Our method yields the saliency map with the form of the Shannon self-information.The experimental comparisons between the proposed and 9 state-of-the-art approaches and the analysis of complexity of our algorithm demonstrate the effectiveness and efficiency of our method.
【Fund】: 国家自然科学基金(No.61005018 No.91120005);; 教育部新世纪优秀人才支持计划(No.NCET-10-0079);; 西北工业大学基础研究基金(No.JC20120237)
【CateGory Index】: TP391.41
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【Co-citations】
Chinese Journal Full-text Database 1 Hits
1 Hu Zhengping,Meng Pengquan(School of Information Science and Engineering,Yanshan University,Qinhuangdao 066004,China);Visual attention salient object detection model based on random walk in a hybrid graph[J];Chinese Journal of Scientific Instrument;2011-07
【Secondary Citations】
Chinese Journal Full-text Database 1 Hits
1 SHAO Jing, GAO Jun(Department of Computer and Information, Laboratory of Image Information Processing Hefei University of Technology, Hefei 230009)(Centre for Biomimetic Sensing and Control Research, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031);Visual Selective Attention Computational Model Based on Synergetic Perception[J];Journal of Image and Graphics;2008-01
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