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利用层次先验估计的显著性目标检测

徐威 唐振民

徐威, 唐振民. 利用层次先验估计的显著性目标检测. 自动化学报, 2015, 41(4): 799-812. doi: 10.16383/j.aas.2015.c140281
引用本文: 徐威, 唐振民. 利用层次先验估计的显著性目标检测. 自动化学报, 2015, 41(4): 799-812. doi: 10.16383/j.aas.2015.c140281
XU Wei, TANG Zhen-Min. Exploiting Hierarchical Prior Estimation for Salient Object Detection. ACTA AUTOMATICA SINICA, 2015, 41(4): 799-812. doi: 10.16383/j.aas.2015.c140281
Citation: XU Wei, TANG Zhen-Min. Exploiting Hierarchical Prior Estimation for Salient Object Detection. ACTA AUTOMATICA SINICA, 2015, 41(4): 799-812. doi: 10.16383/j.aas.2015.c140281

利用层次先验估计的显著性目标检测

doi: 10.16383/j.aas.2015.c140281
基金项目: 

国家自然科学基金(61473154)资助

详细信息
    作者简介:

    徐威 南京理工大学计算机科学与工程学院博士研究生.2009年获得南京理工大学计算机科学与技术学院学士学位.主要研究方向为图像处理,计算机视觉.E-mail:xuwei904@163.com

    通讯作者:

    唐振民 南京理工大学计算机科学与工程学院教授.2002年获得南京理工大学计算机系模式识别与智能系统方向博士学位.主要研究方向为智能机器人,模式识别,图像处理与智能信息系统.本文通信作者.E-mail:tzm.cs@njust.edu.cn

Exploiting Hierarchical Prior Estimation for Salient Object Detection

Funds: 

Supported by National Natural Science Foundation of China(61473154)

  • 摘要: 有效的显著性目标检测在计算机视觉领域一直是具有挑战性的问题.本文首先对图像进行树滤波处理,采用Quick shift方法将其分解为超像素,再通过仿射传播聚类把超像素聚集为代表性的类.与以往方法不同,本文提出根据各类中拥有的超像素的类内和类间的空间离散程度及其位于图像边界的数目,自适应地估计先验背景,并提取条状背景区域;由目标性度量(Objectness measure)粗略地描述前景范围后,通过与各类之间的空间交互信息,估计先验前景;再经过连通区域优化前景与背景信息.最后,综合考虑各超像素与先验背景和前景在CIELab颜色空间的距离,并进行显著性中心加权,得到显著图.在MSRA-1000和复杂的SOD数据库上的实验结果表明,本文算法能准确、完整地检测出显著性目标,优于21种State-of-the-art算法,包括基于部分类似原理的方法.
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出版历程
  • 收稿日期:  2014-05-04
  • 修回日期:  2014-12-03
  • 刊出日期:  2015-04-20

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