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基于加权的K近邻线性混合显著性目标检测

李炜 李全龙 刘政怡

引用本文: 李炜, 李全龙, 刘政怡. 基于加权的K近邻线性混合显著性目标检测[J]. 电子与信息学报, doi: 10.11999/JEIT190093 shu
Citation:  Wei LI, Quanlong LI, Zhengyi LIU. Salient Object Detection Using Weighted K-nearest Neighbor Linear Blending[J]. Journal of Electronics and Information Technology, doi: 10.11999/JEIT190093 shu

基于加权的K近邻线性混合显著性目标检测

    作者简介: 李炜: 女,1969年生,教授,研究方向为计算机视觉;
    李全龙: 男,1995年生,硕士生,研究方向为图像显著性检测;
    刘政怡: 女,1978年生,副教授,研究方向为计算机视觉;
    通讯作者: 刘政怡, liuzywen@ahu.edu.cn
摘要: 显著性目标检测旨在于一个场景中自动检测能够引起人类注意的目标或者区域,在自底向上的方法中,基于多核支持向量机(SVM)的集成学习取得了卓越的效果。然而,针对每一张要处理的图像,该方法都要重新训练,每一次训练都是非常耗时的。因此,该文提出一个基于加权的K近邻线性混合(WKNNLB)显著性目标检测方法:利用现有的方法来产生初始的弱显著图并获得训练样本,引入加权的K近邻(KNN)模型来预测样本的显著性值,该模型不需要任何训练过程,仅需选择一个最优的K值和计算与测试样本最近的K个训练样本的欧式距离。为了减少选择K值带来的影响,多个加权的K近邻模型通过线性混合的方式融合来产生强的显著图。最后,将多尺度的弱显著图和强显著图融合来进一步提高检测效果。在常用的ASD和复杂的DUT-OMRON数据集上的实验结果表明了该算法在运行时间和性能上的有效性和优越性。当采用较好的弱显著图时,该算法能够取得更好的效果。

English

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  • 图 1  本文方法的框架图

    图 2  强显著模型示意图

    图 3  加权k近邻模型示意图

    图 4  m取不同值在ASD数据集上的F-measure曲线

    图 5  n取不同值在ASD数据集上的F-measure曲线

    图 6  各种方法产生的显著图的视觉对比

    图 7  各方法及其提高在ASD和DUT-OMRON数据集上的P-R曲线

    图 8  WKNNLB和BLSVM在ASD和DUT-OMRON数据集上的P-R曲线

    表 1  特征${{f}}_i^j$取值(65维)

    特征维度序号特征维度大小取值范围
    0~2平均RGB值3[0,1]
    3~5平均CIELab值3[0,1]
    6~64LBP直方图值59[0,1]
    下载: 导出CSV

    表 2  5种经典方法及其提高在F-度量值的对比

    算法ITIT+LRMRLRMR+GCGC+MRMR+MBDMBD+
    ASD0.3810.5420.7270.8290.8110.8480.8690.8760.8550.867
    DUT-OMRON0.3430.5410.4980.5450.5200.5540.5740.5760.8500.854
    下载: 导出CSV

    表 3  WKNNLB和BLSVM在4个数据集上F-度量和运行时间(s)对比

    ASDSED2PASCAL-SDUT-OMRON
    F-measureTimeF-measureTimeF-measureTimeF-measureTime
    WKNNLB0.82040580.7583320.65550000.53430864
    BLSVM0.81080930.7407200.651111840.52465120
    下载: 导出CSV
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文章相关
  • 通讯作者:  刘政怡, liuzywen@ahu.edu.cn
  • 收稿日期:  2019-02-01
  • 录用日期:  2019-06-03
  • 网络出版日期:  2019-06-12
通讯作者: 陈斌, bchen63@163.com
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