基于卷积神经网络和语义相关的协同显著性检测  

CNN and semantic correlation based co-saliency detection

作　　者：张华迪 樊玮[1] 黄睿 Zhang Huadi;Fan Wei;Huang Rui(College of Computer Science&Technology,Civil Aviation University of China,Tianjin 300300,China)

机构地区：[1]中国民航大学计算机科学与技术学院,天津300300

出　　处：《计算机应用研究》2020年第12期3811-3814,3819,共5页Application Research of Computers

基　　金：国家自然科学基金资助项目(U1333109)。

摘　　要：针对目前协同显著性检测方法中存在的语义特征类相差悬殊的物体被误检测为协同对象等问题,提出了一种基于卷积神经网络和语义相关的协同显著性检测算法CSCCD。首先,采用引导超像素滤波方法对SLIC分割出的超像素区域和DSS生成的显著性区域进行处理,清晰地显示了目标边界轮廓;然后使用Mask R-CNN提取语义特征,给出了图像语义特征和语义一致性的定义,并针对提取语义特征过程中出现的同一语义类别的物体在不同形态下被检测为不同语义类别的问题,提出了图像组语义相关类的概念,在此概念的基础上定义了图像组语义关联类,解决了多幅图像的语义关联问题;最后融合显著性检测区域和图像组语义一致性区域得到协同显著性检测结果。在公开基准数据集上的实验结果表明,该算法能够有效凸显目标整体及轮廓,在客观量化方面的综合性能有明显提升。To solve the problems that the objects with different semantic classes are identified as co-salient objects in current co-saliency detection methods,this paper proposed a CNN and semantic correction-based co-saliency detection method(CSCCD).The proposed method first adopted the guided super pixel filter to process the super pixels obtained by SLIC and the saliency results generated by DSS,which showed clear object boundaries.Then it utilized Mask R-CNN to extract semantic features.It proposed the definitions of image semantic feature and semantic consistency.It also defined the image group semantic correction to solve the problem that detected the objects with different pose belonging to a semantic class as different semantic classes.With the concept,this paper defined image group semantic correlation class,solving semantic correlation problem of multiple images.It generated the final co-saliency detection results by fusing the saliency detection regions with the image group semantic consistent regions.The experimental results on public benchmark datasets show that this algorithm can effectively highlight the whole and outline of the object,and its comprehensive performance in objective quantification is obviously improved.

关 键 词：协同显著性检测 深度学习 卷积神经网络 图像组语义相关类 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]