计算机科学 ›› 2019, Vol. 46 ›› Issue (3): 48-52.doi: 10.11896/j.issn.1002-137X.2019.03.006

• 综述 • 上一篇    下一篇

自底向上的显著性目标检测研究综述

吴加莹1,杨赛1,2,堵俊1,林宏达1   

  1. (南通大学电气工程学院 江苏 南通 226019)1
    (南通先进通信技术研究院 江苏 南通 226019)2
  • 收稿日期:2018-03-13 修回日期:2018-07-12 出版日期:2019-03-15 发布日期:2019-03-22
  • 作者简介:吴加莹女,硕士,主要研究方向为机器视觉与模式识别;杨赛女,博士,讲师,主要研究方向为计算机视觉与机器学习;堵俊男,教授,主要研究方向为信号检测、信号及系统分析,E-mail:du.j@ntu.edu.cn(通信作者);林宏达男,主要研究方向为计算机视觉与机器学习。
  • 基金资助:
    江苏省普通高校自然科学基金(16KJB520037),南通大学-南通智能信息技术联合研究中心(KFKT2017A02)资助

Review of Bottom-up Salient Object Detection

WU Jia-ying1,YANG Sai1,2,DU Jun1,LIN Hong-da1   

  1. (School of Electrical Engineering,Nantong University,Nantong,Jiangsu 226019,China)1
    (Nantong Research Institute for Advanced Communication Technologies,Nantong,Jiangsu 226019,China)2
  • Received:2018-03-13 Revised:2018-07-12 Online:2019-03-15 Published:2019-03-22

摘要: 文中对显著性目标检测(Salient Object Detection)领域内的国内外发展现状进行了综述。首先,介绍了显著性目标检测的研究背景和发展历程;然后,根据各个模型所使用特征的不同,分别从手工设计特征和深度学习特征这两个方面对显著性计算进行综述,在论述基于手工设计特征的显著性计算的研究进展时,将其细分为基于对比度先验的显著性计算、基于前景先验的显著性计算以及基于背景先验的显著性计算3个子类,并对每个类别中的若干典型算法的建模思路进行了描述;最后,进行分析与总结,并指出显著性目标检测领域仍需解决的问题及未来的研究方向。

关键词: 深度学习, 显著图融合, 显著性目标检测, 显著性先验

Abstract: This paper reviewed the current development status at home and abroad in the field of salient object detection.Firstly,this paper introduced the research background and development process of salient object detection.Then,aiming at the difference of the features used by each saliency model,it summarized the saliency calculation from two aspects of hand-crafted features and deep learning features.While the saliency calculation based on hand-crafted features are addressed,it is further classified into the following three subcategories,i.e.the saliency calculation based on contrast prior,the saliency calculation based on foreground prior,and the saliency calculation based on back ground prior.Meanwhile,this paper elaborated the basic ideas of saliency modeling in each subcategory.Finally,it discussed the problems to be solved and further research directions of salient object detection.

Key words: Deep learning, Fusion of saliency maps, Saliency prior, Salient object detection

中图分类号: 

  • TP391
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