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首页> 外文期刊>Multimedia, IEEE Transactions on >Person Reidentification via Ranking Aggregation of Similarity Pulling and Dissimilarity Pushing
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Person Reidentification via Ranking Aggregation of Similarity Pulling and Dissimilarity Pushing

机译：通过相似度拉和不同度推的排序聚合进行人员识别

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摘要

Person reidentification is a key technique to match different persons observed in nonoverlapping camera views. Many researchers treat it as a special object-retrieval problem, where ranking optimization plays an important role. Existing ranking optimization methods mainly utilize the similarity relationship between the probe and gallery images to optimize the original ranking list, but seldom consider the important dissimilarity relationship. In this paper, we propose to use both similarity and dissimilarity cues in a ranking optimization framework for person reidentification. Its core idea is that the true match should not only be similar to those strongly similar galleries of the probe, but also be dissimilar to those strongly dissimilar galleries of the probe. Furthermore, motivated by the philosophy of multiview verification, a ranking aggregation algorithm is proposed to enhance the detection of similarity and dissimilarity based on the following assumption: the true match should be similar to the probe in different baseline methods. In other words, if a gallery blue image is strongly similar to the probe in one method, while simultaneously strongly dissimilar to the probe in another method, it will probably be a wrong match of the probe. Extensive experiments conducted on public benchmark datasets and comparisons with different baseline methods have shown the great superiority of the proposed ranking optimization method.
机译：人员重新识别是匹配在不重叠摄像机视图中观察到的不同人员的关键技术。许多研究人员将其视为特殊的对象检索问题，其中排名优化起着重要作用。现有的排名优化方法主要利用探测器图像与画廊图像之间的相似关系来优化原始排名列表，但很少考虑重要的相似关系。在本文中，我们建议在排名优化框架中使用相似性和不相似性提示进行人员重新识别。其核心思想是，真正的匹配不仅应与探针的那些非常相似的画廊相似，而且应与探针的那些非常相似的画廊相异。此外，受多视图验证原理的启发，基于以下假设，提出了一种排序聚合算法，以增强对相似性和不相似性的检测：在不同的基线方法中，真实匹配应与探针相似。换句话说，如果长廊蓝色图像在一种方法中与探针非常相似，而在另一种方法中与探针非常相似，则可能是探针的错误匹配。在公共基准数据集上进行的大量实验以及与不同基准方法的比较表明，所提出的排名优化方法具有很大的优势。

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  • 来源
    《Multimedia, IEEE Transactions on》 |2016年第12期|2553-2566|共14页
  • 作者

    Mang Ye; Chao Liang; Yi Yu; Zheng Wang; Qingming Leng; Chunxia Xiao; Jun Chen; Ruimin Hu;

  • 作者单位

    State Key Laboratory of Software Engineering, Collaborative Innovation Center of Geospatial Technology, National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, China;

    State Key Laboratory of Software Engineering, Collaborative Innovation Center of Geospatial Technology, National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, China;

    Digital Content and Media Sciences Research Division, National Institute of Informatics, Tokyo, Japan;

    State Key Laboratory of Software Engineering, Collaborative Innovation Center of Geospatial Technology, National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, China;

    School of Information Science and Technology, Jiujiang University, Jiujiang, China;

    State Key Lab of Software Engineering, School of Computer Science, Wuhan University, Wuhan, China;

    State Key Laboratory of Software Engineering, Collaborative Innovation Center of Geospatial Technology, National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, China;

    State Key Laboratory of Software Engineering, Collaborative Innovation Center of Geospatial Technology, National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, China;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Probes; Optimization methods; Cameras; Feature extraction; Image retrieval; Multimedia communication;

    机译：探头;优化方法;相机;特征提取;图像检索;多媒体通信;

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