Computer Engineering and Applications ›› 2016, Vol. 52 ›› Issue (15): 217-221.

### Co-occurrence histogram based saliency detection method enhanced by entropy

GAO Kaiyuan, WEI Ning, DONG Fangmin

1. Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang, Hubei 443002, China
• Online:2016-08-01 Published:2016-08-12

### 基于信息量增强的共生直方图显著性检测算法

1. 湖北省水电工程智能视觉监测重点实验室（三峡大学），湖北 宜昌 443002

Abstract: This paper proposes an image enhanced saliency detection method which overwhelms the shortage at the existing co-occurrence histogram based methods that are easily to be influenced by the high contrast edge object in background regions. The motivation of the method is inspired from the fact that the difference between the co-occurrence histogram 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.