摘要
针对复杂背景下显著性检测方法不能够有效地抑制背景,进而准确地检测目标这一问题,提出了超像素内容感知先验的多尺度贝叶斯显著性检测方法.首先,将目标图像分割为多尺度的超像素图,在每个尺度上引入内容感知的对比度先验、中心位置先验、边界连通背景先验来计算单一尺度上的目标显著值;其次,融合多个尺度的内容感知先验显著值生成一个粗略的显著图;然后,将粗略显著图值作为先验概率,根据颜色直方图和凸包中心先验计算观测似然概率,再使用多尺度贝叶斯模型来获取最终显著目标;最后,使用了3个公开的数据集、5种评估指标、7种现有的方法进行对比实验,结果表明本文方法在显著性目标检测方面具有更好的表现.
Existing saliency detection methods can not suppress the background effectively and detect the salient object accurately in complex background,a method of superpixel content-aware priors based multi-scale Bayesian saliency detection is proposed.Firstly,the image containing object is segmented into multi-scale superpixel maps,then the content-aware priors of contrast priors,center position priors,and boundary connected background priors are introduced on each scale to calculate the salient object values on a single scale;Secondly,the content-aware priors values of the various scales generate a rough saliency map;Thirdly,the rough saliency map value is used as the prior probability,and the likelihood is calculated according to the color histogram and the convex hull center,using the multi-scale Bayesian model to obtain the final salient object;Finally,three public data sets,five evaluation indicators,and seven existing methods are used for comparative experiments.The experiments show that the method has better performance in the detection of salient objects.
作者
张荣国
贾玉闪
胡静
刘小君
李晓明
ZHANG Rong-guo;JIA Yu-shan;HU Jing;LIU Xiao-jun;LI Xiao-ming(School of Computer Science and Technology,Taiyuan University of Science and Technology,Taiyuan,Shanxi 030024,China;School of Mechanical Engineering,Hefei University of Technology,Hefei,Anhui 230009,China)
出处
《电子学报》
EI
CAS
CSCD
北大核心
2020年第8期1509-1515,共7页
Acta Electronica Sinica
基金
国家自然科学基金(No.51875152)
山西省自然科学基金(No.201801D121134)
晋城市科技局资助项目(No.201501004-5)。
