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基于自适应背景模板与空间先验的显著性物体检测方法

林华锋 李静 刘国栋 梁大川 李东民

林华锋, 李静, 刘国栋, 梁大川, 李东民. 基于自适应背景模板与空间先验的显著性物体检测方法. 自动化学报, 2017, 43(10): 1736-1748. doi: 10.16383/j.aas.2017.c160431
引用本文: 林华锋, 李静, 刘国栋, 梁大川, 李东民. 基于自适应背景模板与空间先验的显著性物体检测方法. 自动化学报, 2017, 43(10): 1736-1748. doi: 10.16383/j.aas.2017.c160431
LIN Hua-Feng, LI Jing, LIU Guo-Dong, LIANG Da-Chuan, LI Dong-Min. Saliency Detection Method Using Adaptive Background Template and Spatial Prior. ACTA AUTOMATICA SINICA, 2017, 43(10): 1736-1748. doi: 10.16383/j.aas.2017.c160431
Citation: LIN Hua-Feng, LI Jing, LIU Guo-Dong, LIANG Da-Chuan, LI Dong-Min. Saliency Detection Method Using Adaptive Background Template and Spatial Prior. ACTA AUTOMATICA SINICA, 2017, 43(10): 1736-1748. doi: 10.16383/j.aas.2017.c160431

基于自适应背景模板与空间先验的显著性物体检测方法

doi: 10.16383/j.aas.2017.c160431
基金项目: 

中央高校基本科研业务费专项资金 NS2015092

详细信息
    作者简介:

    林华锋 南京航空航天大学大学硕士研究生.主要研究方向为计算机图像处理.E-mail:nuaalhf@163.com

    刘国栋 江苏省委党校硕士研究生.主要研究方向为计算机仿真, 电子信息与图像处理.E-mail:liuguodongzdhxb@gmail.com

    梁大川 南京航空航天大学大学硕士研究生.主要研究方向为计算机图像处理.E-mail:dacliang@nuaa.edu.cn

    李东民 南京航空航天大学大学硕士研究生.主要研究方向为计算机图像处理.E-mail:nuaa lidm@163.com

    通讯作者:

    李静 南京航空航天大学计算机科学与技术学院副教授.2004年获南京大学计算机科学与工程系博士学位.主要研究方向为数据挖掘, 计算机图像处理.本文通信作者, E-mail:jingli@nuaa.edu.cn

Saliency Detection Method Using Adaptive Background Template and Spatial Prior

Funds: 

Fundamental Research Funds for the Central Universities NS2015092

More Information
    Author Bio:

    Master student at the College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics. His main research interest is image processing

    Master student at Jiangsu Provincial Commission Party School. His research interest covers computer simulation, electronic information, and image processing

    Master student at the College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics. His main research interest is image processing

    Master student at the College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics. His main research interest is image processing

    Corresponding author: LI Jing  Associate professor at Nanjing University of Aeronautics and Astronautics. She received her Ph. D. degree at the College of Computer Science and Technology, Nanjing University in 2004. Her research interest covers data mining and image processing. Corresponding author of this paper, E-mail:jingli@nuaa.edu.cn
  • 摘要: 目前,显著性检测已成为国内外计算机视觉领域研究的一个热点,但现有的显著性检测算法大多无法有效检测出位于图像边缘的显著性物体.针对这一问题,本文提出了基于自适应背景模板与空间先验的显著性物体检测方法,共包含三个步骤:第一,根据显著性物体在颜色空间上具有稀有性,获取基于自适应背景模板的显著图.将图像分割为超像素块,提取原图的四周边界作为原始背景区域.利用设计的自适应背景选择策略移除原始背景区域中显著的超像素块,获取自适应背景模板.通过计算每个超像素块与自适应背景模板的相异度获取基于自适应背景模板的显著图.并采用基于K-means的传播机制对获取的显著图进行一致性优化;第二,根据显著性物体在空间分布上具有聚集性,利用基于目标中心优先与背景模板抑制的空间先验方法获得空间先验显著图.第三,将获得的两种显著图进行融合得到最终的显著图.在公开数据集MSRA-1000、SOD、ECSSD和新建复杂数据集CBD上进行实验验证,结果证明本文方法能够准确有效地检测出图像中的显著性物体.
    1)  本文责任编委 王聪
  • 图  1  SCB框架图

    Fig.  1  The framework of SCB salient detection method

    图  2  基于自适应背景模板的显著图

    Fig.  2  Saliency maps based on adaptive background template

    图  3  利用传播机制优化基于自适应背景模板显著图

    Fig.  3  Saliency maps based on propagation mechanism

    图  4  基于空间先验显著图

    Fig.  4  Saliency maps based on spatial prior

    图  5  针对显著性物体位于图像边缘的图像, 显著图的视觉比较结果

    Fig.  5  Visual comparison of previous methods, our method and ground truth for the image whose salient object locates at the border

    图  6  显著图的视觉比较结果

    Fig.  6  Visual comparison of previous methods, our method and ground truth

    图  7  不同算法的准确率-召回率曲线与F-measure值柱状图

    Fig.  7  Quantitative results of the precision/recall curves and F-measure metrics

    图  8  针对MSRA-1000、SOD、ECSSD与CBD数据集, 不同显著性检测方法的MAE柱状图

    Fig.  8  Quantitative results of the MAE value of MSRA-1000, SOD, ECSSD and CBD

    图  9  针对MSRA-1000数据集, 提出的SCB方法各个组成部分的准确率-召回率曲线

    Fig.  9  Quantitative results of each component of SCB method in MSRA-1000 dataset

    图  10  针对Co-saliency pairs数据集, 基于Web的协同显著图

    Fig.  10  The co-saliency map based on web in co-saliency pairs dataset

    表  1  平均检测时间对比表

    Table  1  The table of contrast result in running times

    方法FTCALRGCGLHS
    编程工具C++MATLABMATLABC++MATLABC++
    时间0.0252.5814.50.099.420.49
    方法GMMCDSRLPSSCB
    编程工具MATLABMATLABMATLABMATLABMATLAB
    时间0.250.143.532.401.12
    下载: 导出CSV
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  • 收稿日期:  2016-05-27
  • 录用日期:  2016-10-26
  • 刊出日期:  2017-10-20

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