摘要
在显著性目标检测中,背景区域和前景区域区分度不高会导致检测结果不理想。针对这一问题,提出一种基于邻域优化机制的图像显著性目标检测算法。首先对图像进行超像素分割;然后在CIELab颜色空间建立对比图和分布图,并通过一种新的合并方式进行融合;最后在空间距离等约束下,建立邻域更新机制,对初始显著性图进行优化。实验对比表明,该算法显著性目标检测效果更好。
In the salient object detection,the detection results are not ideal when the difference between the background region and the foreground region is not obvious.To address this problem,we propose a saliency object detection algorithm based on neighborhood optimization mechanism.Firstly,the image is segmented by super-pixels.Then,the contrast map and distribution map are established in the CIELab color space and they are merged by a new merging method.Finally,under the constraints such as spatial distance,a neighborhood updating mechanism is established to optimize the initial salient maps.Experimental results show that the algorithm is more effective in salient object detection.
作者
魏伟一
王瑜
窦镭响
文雅宏
WEI Wei-yi;WANG Yu;DOU Lei-xiang;WEN Ya-hong(College of Computer Science and Engineering,Northwest Normal University,Lanzhou 730070,China)
出处
《计算机工程与科学》
CSCD
北大核心
2019年第8期1459-1465,共7页
Computer Engineering & Science
基金
国家自然科学基金(61861040)
甘肃省科技计划资助项目(17YF1FA119
关键词
显著性目标
邻域优化
超像素
salient object
neighborhood optimization
super-pixels
