超像素内容感知先验的多尺度贝叶斯显著性检测方法

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.

Superpixel Content-Aware Priors Based Multi-Scale Bayesian Saliency Detection

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.