Saliency detection via fused global and local information
- 分享到:
摘要：
为提高低对比度、复杂自然图像显著性检测的准确率和泛化性能,提出一种贝叶斯框架下的全局和局部信息融合的显著性检测模型.首先,构建深度卷积自编码网络,采用对称编解码结构,监督学习图像全局特征,得到全局显著图;然后,根据全局显著图产生前景和背景码本,利用局部约束线性编码算法进行编码,采用稀疏编码描述局部特征,产生局部显著图;最后,提出采用贝叶斯框架,将全局和局部信息融合,生成最终显著图.实验结果表明,所提模型在ECSSD,DUT-OMRON和PASCAL数据集上F-measure值分别为76.53%、59.45%和72.52%,MAE值分别为0.14328、0.13787和0.18105,且能够有效对低对比度、复杂真实自然图像进行显著性检测.
Aiming at improving the accuracy and generalization ability of natural images with low contrast and complex background,a saliency detection model based on Bayesian framework is proposed via fusing global and local information.Firstly,a deep convolution autoencoder network was constructed,and symmetrical encoder and decoder structure was adopted to supervise learning the global features,and global saliency map was generated;Secondly,foreground and background codebooks were obtained from the global saliency map,and the locality-constrained linear coding method was used for encoding,and the sparse coding was employed to describe the local features,so local saliency map was generated;Finally,the Bayesian framework was adopted to integrate the global with local information,and the final saliency map was thus obtained.The experimental results show that the F-measure of our model on ECSSD,DUT-OMRON and PASCAL datasets is 76.53%,59.45%and 72.52%respectively,and the MAE is 0.14328,0.13787 and 0.18105 respectively.Furthermore,our method can effectively detect the salient object in real natural images with the low contrast and complex background.
作者：
刘尚旺 赵欣莹 杨磊
Liu Shangwang;Zhao Xinying;Yang Lei(College of Computer and Information Engineering,Henan Normal University,Xinxiang 453007,China;College of Tourism,Henan Normal University,Xinxiang 453007,China)
机构地区：
betway官方app 计算机与信息工程学院 betway官方app 旅游学院
出处：
《betway官方app 学报：自然科学版》 CAS 北大核心 2020年第3期26-33,共8页
基金：
河南省科技攻关项目(192102210290) 河南省高等学校重点科研项目(15A520082) betway官方app 博士科研启动基金(qd12138).
关键词：
显著性检测 贝叶斯框架 稀疏编码 深度卷积自编码网络
saliency detection Bayesian framework sparse coding deep convolutional autoencoder network
分类号：
TP391 [自动化与计算机技术—计算机应用技术]
