基于优化查询的改进显著性检测算法  

Improved Saliency Detection Algorithm Based on Optimized Query

作　　者：王慧玲 宋鑫怡 杨颖 WANG Huiling;SONG Xinyi;YANG Ying(College of Computer and Information Engineering,Fuyang Normal University,Fuyang 236037,China)

机构地区：[1]阜阳师范大学计算机与信息工程学院,安徽阜阳236037

出　　处：《吉林大学学报（信息科学版）》2020年第3期319-324,共6页Journal of Jilin University（Information Science Edition）

基　　金：国家自然科学基金资助项目(61906044);安徽省教育厅自然科学重点基金资助项目(KJ2019A0529;KJ2019A0536);阜阳师范大学2019年度青年人才重点基金资助项目(RCXM201906);安徽省大学生创新创业训练计划基金资助项目(201810371056)。

摘　　要：传统的基于图的流行排序算法,仅利用图像的边界作为背景查询,其查询选择的准确率直接影响算法的结果,为此提出一种改进算法,利用现有算法的检测结果为基础,对前景与背景种子点的选取进行优化。首先,对图像进行超像素分割,充分利用图像的中层信息;其次,对图像利用流行排序算法计算图像显著图;最后,对显著性结果进行处理,选取更优的查询点,得到最终显著图。在CSSD(Complex Scene Saliency Datase)和ECSSD(Extended Complex Scene Saliency Datese)数据集上与8种算法进行比较,实验结果表明,该算法具有更高的检测准确率。To overcome the shortcomings of the traditional popular graph-based sorting algorithms,only using the image boundary as the background query,the accuracy of the query selection directly affects the results of the algorithm,an improved algorithm is proposed.Based on the detection results of current algorithms,the selection of foreground and background seeds is optimized.Firstly,super-pixel segmentation is performed on the image to make full use of the middle-level information of the image.Secondly,the image saliency map is calculated with the popular ranking algorithm.Finally,the saliency result is processed to select better query points and obtain the final saliency map.Compared to eight algorithms on CSSD(Complex Scene Saliency Datase)and ECSSD(Extended Complex Scene Saliency Datese)datasets,the experimental results show that the proposed algorithm has higher detection accuracy.

关 键 词：流行排序 显著性检测 查询优化 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]