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基于多图流形排序的图像显著性检测

于明 李博昭 于洋 刘依

于明, 李博昭, 于洋, 刘依. 基于多图流形排序的图像显著性检测. 自动化学报, 2019, 45(3): 577-592. doi: 10.16383/j.aas.2018.c170441
引用本文: 于明, 李博昭, 于洋, 刘依. 基于多图流形排序的图像显著性检测. 自动化学报, 2019, 45(3): 577-592. doi: 10.16383/j.aas.2018.c170441
YU Ming, LI Bo-Zhao, YU Yang, LIU Yi. Image Saliency Detection With Multi-graph Model and Manifold Ranking. ACTA AUTOMATICA SINICA, 2019, 45(3): 577-592. doi: 10.16383/j.aas.2018.c170441
Citation: YU Ming, LI Bo-Zhao, YU Yang, LIU Yi. Image Saliency Detection With Multi-graph Model and Manifold Ranking. ACTA AUTOMATICA SINICA, 2019, 45(3): 577-592. doi: 10.16383/j.aas.2018.c170441

基于多图流形排序的图像显著性检测

doi: 10.16383/j.aas.2018.c170441
基金项目: 

天津市科技计划 17ZLZDZF00040

天津市科技计划 14RCGFGX00846

河北省自然科学基金 F2015202239

天津市科技计划 15ZCZDNC00130

详细信息
    作者简介:

    于明  河北工业大学计算机科学与软件学院教授.1999年于北京理工大学获得通信与信息系统专业博士学位.主要研究方向为语音与图像视觉信息融合的生物特征识别, 图像数学变换、图像与视频编码的高效算法.E-mail:yuming@hebut.edu.cn

    李博昭  河北工业大学计算科学与软件学院硕士研究生.2015年获得河北工业大学信息与计算科学专业学士学位.主要研究方向为图像显著性检测.E-mail:201522102010@hebut.edu.cn

    刘依  河北工业大学计算机科学与软件学院讲师.2003年获得河北工业大学计算机应用技术专业硕士学位.主要研究方向为图像处理与识别、图像显著性检测.E-mail:liuyi@scse.hebut.edu.cn

    通讯作者:

    于洋  河北工业大学计算机科学与软件学院讲师.2012年获得河北工业大学微电子学与固体电子学博士学位.主要研究方向为图像处理与分析、模式识别、计算机视觉, 智能交通系统.本文通信作者.E-mail:yuyang@scse.hebut.edu.cn

Image Saliency Detection With Multi-graph Model and Manifold Ranking

Funds: 

Supported by Tianjin Science and Technology Planning Project 17ZLZDZF00040

Supported by Tianjin Science and Technology Planning Project 14RCGFGX00846

Hebei Province Natural Science Foundation F2015202239

Supported by Tianjin Science and Technology Planning Project 15ZCZDNC00130

More Information
    Author Bio:

      Professor at School of Computer Science and Engineering, Hebei University of Technology. He obtained his doctoral degree in communication and information systems from Beijing Institute of Technology in 1999. His research interest covers biometrics of voice and image vision information fusion, image mathematical transformation, efficient algorithm of image and video coding

      Master student at School of Computer Science and Engineering, Hebei University of Technology. She received her bachelor degree from Hebei University of Technology in 2015. Her main research interest is image saliency detection

      Lecturer at School of Computer Science and Engineering, Hebei University of Technology. She received her master degree in computer applied technology from Hebei University of Technology in 2011. Her research interest covers image processing and recognition, image saliency detection

    Corresponding author: YU Yang   Lecturer at School of Computer Science and Engineering, Hebei University of Technology. He received his Ph. D. degree in microelectronics and solid state electronics from Hebei University of Technology in 2012. His research interest covers image processing and analysis, pattern recognition, computer vision and intelligent transportation system. Corresponding author of this paper
  • 摘要: 针对现有图像显著性检测算法中显著目标检测不完整和显著目标内部不均匀的问题,本文提出了一种基于多图流形排序的图像显著性检测算法.该算法以超像素为节点构造KNN图(K nearest neighbor graph)模型和K正则图(K regular graph)模型,分别在两种图模型上利用流形排序算法计算超像素节点的显著性值,并将每个图模型中超像素节点的显著值加权融合得到最终的显著图.在公开的MSRA-10K、SED2和ECSSD三个数据集上,将本文提出的算法与当前流行的14种算法进行对比,实验结果显示本文算法能够完整地检测出显著目标,并且显著目标内部均匀光滑.
    1)  本文责任编委 杨健
  • 图  1  全局前景假设和边界先验的比较

    Fig.  1  Comparison of global foreground prior and background prior

    图  2  不同图模型生成的显著图比较

    Fig.  2  Comparison of saliency maps obtained by different graphs

    图  3  MSRA-10K数据库上实验结果

    Fig.  3  Experimental results on the MSRA-10K database

    图  4  MSRA-10K数据库上的P-R曲线、ROC曲线和F值

    Fig.  4  P-R curves, ROC curves, and F values on the MSRA-10K database

    图  6  SED2数据库上的P-R曲线、ROC曲线和F值

    Fig.  6  P-R curves, ROC curves, and F values on the SED2 database

    图  8  ECSSD数据库上的P-R曲线、ROC曲线和F值

    Fig.  8  P-R curves, ROC curves, and F values on the ECSSD database

    图  5  SED2数据库上实验结果

    Fig.  5  Experimental results on the SED2 database

    图  7  ECSSD数据库上实验结果

    Fig.  7  Experimental results on the ECSSD database

    表  1  K正则图模型、KNN图模型和K正则图模型+ KNN图模型比较

    Table  1  The comparison of K regular graph, KNN graph, and K regular graph + KNN graph

    MSRA-10K SED2 ECSSD
    P R F P R F P R F
    K正则 0.8557 0.7705 0.7948 0.8293 0.6990 0.7524 0.7053 0.6950 0.6344
    KNN 0.8627 0.7468 0.7897 0.7918 0.6034 0.6837 0.7231 0.7229 0.6677
    K正则+ KNN 0.8723 0.7962 0.8228 0.8080 0.7253 0.7504 0.7279 0.7476 0.6828
    下载: 导出CSV

    表  2  边界假设和全局前景假设比较

    Table  2  The comparison of boundary assumption and global foreground assumption

    MSRA-10K SED2 ECSSD
    P R F P R F P R F
    边界 0.8815 0.6670 0.7846 0.7267 0.4883 0.6156 0.7927 0.5830 0.6768
    全局 0.8723 0.7962 0.8228 0.8080 0.7253 0.7504 0.7279 0.7476 0.6828
    下载: 导出CSV

    表  3  初始显著图和优化后显著图的比较

    Table  3  The comparison of original saliency maps and refined saliency maps

    MSRA-10K SED2 ECSSD
    P R F P R F P R F
    初始图 0.8723 0.7962 0.8228 0.8080 0.7253 0.7504 0.7279 0.7476 0.6828
    优化图 0.8796 0.7880 0.8327 0.8210 0.6729 0.7456 0.7587 0.7152 0.7034
    下载: 导出CSV

    表  4  各种方法在不同数据库上的AUC值和F值

    Table  4  The AUC and F values of the various methods on different databases

    AUC值 F值
    MSRA-10K SED2 ECSSD MSRA-10K SED2 ECSSD
    HC[8] 0.8589 0.8839 0.7022 0.6370 0.7113 0.4186
    RC[8] 0.9406 0.8580 0.8919 0.8150 0.7151 0.6802
    AC[6] 0.7767 0.8430 0.6750 0.5636 0.7012 0.4026
    HS[7] 0.9353 0.8960 0.8833 0.8128 0.7298 0.6657
    SR[12] 0.6716 0.7290 0.6054 0.3454 0.4307 0.3001
    FT[10] 0.7797 0.8310 0.6591 0.5884 0.6872 0.3959
    MSS[11] 0.8607 0.8768 0.7678 0.6549 0.7125 0.4918
    MR[22] 0.9404 0.8618 0.8858 0.8219 0.7227 0.6868
    GS[18] 0.9527 0.9055 0.8834 0.7987 0.7546 0.6268
    BFSS[13] 0.8807 0.8932 0.8625 0.7570 0.7755 0.6080
    RW[20] 0.3976 0.8364 0.7252 0.4614 0.4868 0.3746
    HDCT[14] 0.8483 0.9036 0.8644 0.8143 0.7804 0.6673
    BMA[15] 0.8605 0.8825 0.7762 0.6318 0.5980 0.4927
    RR[21] 0.9423 0.8674 0.8887 0.8219 0.7197 0.6882
    本文算法 0.9532 0.8937 0.9114 0.8327 0.7456 0.7034
    下载: 导出CSV

    表  5  各种方法平均运行时间

    Table  5  The average runtimes of different methods

    SR FT MSS MR GS BFSS RW BMA RR HS HC RC AC HDCT Ours
    MATLAB 0.02 0.04 1.04 1.10 0.21 7.25 1.33 0.002 1.70 1.30
    C++ 0.39 0.01 0.25 0.18 4.12
    下载: 导出CSV
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  • 收稿日期:  2017-08-02
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