计算机工程与应用2016，Vol.52Issue(15)：217-221,5.DOI:10.3778/j.issn.1002-8331.1510-0174
基于信息量增强的共生直方图显著性检测算法
Co-occurrence histogram based saliency detection method enhanced by entropy
摘要
Abstract
This paper proposes an image enhanced saliency detection method which overwhelms the shortage at the exist-ing co-occurrence histogram based methods that are easily to be influenced by the high contrast edge object in back-ground regions. The motivation of the method is inspired from the fact that the difference between the co-occurrence histo-gram distributions indexed from salient regions and background edge regions is very large. According to it, the method uses the entropy to describe the distribution complexity and measures their difference. In order to achieve the purpose of enhancing the salient region and inhibiting the edge of background region, the entropy is multiplied to the saliency value in original algorithm in each channel. The experiments on the AIM data set show that the proposed saliency model is more accurate and robust than original models. And the proposed model can improve the AUC value from 0.7208 to 0.7311 through the ROC curve.关键词
视觉显著性/图像共生直方图/显著性检测Key words
visual saliency/image co-occurrence histogram/saliency detection分类
计算机与自动化引用本文复制引用
高开源,魏宁,董方敏..基于信息量增强的共生直方图显著性检测算法[J].计算机工程与应用,2016,52(15):217-221,5.基金项目
国家自然科学基金（No.61202141，No.61272236，No.61272237）。 （）
