文献类型：期刊文献

中文题名：基于颜色与梯度布尔特征融合的图像显著性检测

英文题名：Saliency Detection Based on Color and Gradient Boolean Features

作者：逄铭雪[1];叶西宁[1];凌志浩[1]

机构：[1]华东理工大学信息科学与工程学院,上海200237

年份：2016

卷号：42

期号：1

起止页码：91

中文期刊名：华东理工大学学报（自然科学版）

外文期刊名：Journal of East China University of Science and Technology

收录：CSTPCD;;Scopus;北大核心:【北大核心2014】；CSCD:【CSCD2015_2016】；

语种：中文

中文关键词：布尔特征;前背景分离;视觉注意

外文关键词：Boolean feature; figure-ground segregation; visual attention

摘要：图像显著性检测存在区域不均匀、显著性值低的问题,本文在对BMS(Boolean Map based Saliency)模型进行研究的基础上提出了基于颜色与梯度布尔特征融合的显著性检测模型(Boolean Map of Color and Gradient based Saliency,BMCG)。根据Gestalt前背景分离的原则,通过随机阈值化颜色通道和梯度通道产生含有图像拓扑结构的二进制布尔新息图,进一步生成视觉注意图并进行线性融合,经过后处理形成显著性图。仿真结果表明BMCG算法比BMS算法的召回率提高了2.12%,准确率提高了4.56%。
There exist non-uniform areas and low saliency score problems in image saliency detection.By analyzing the model of Boolean map based saliency,this paper proposes the Boolean maps of color and gradient based saliency model（BMCG）.According to Gestalt principle of figure-ground segregation,the Boolean maps with the topological structure are generated via the random threshold color channels and gradient channel.Furthermore,these Boolean maps are refined into the attention maps of visual and are linearly combined to generate the saliency map via post-process.The simulation results show that BMCG algorithm is better than BMS algorithm,improving the recall rate of 2.12% and precision rate of 4.56%.