Hierarchical Gaussian Descriptor for Person Re-identification
Hierarchical Gaussian Descriptor for Person Re-identification
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DOI：
10.1109/cvpr.2016.152
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发表时间：
2016-06
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通讯作者：
Tetsu Matsukawa;Takahiro Okabe;Einoshin Suzuki;Yoichi Sato
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文献类型：
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作者：
Tetsu Matsukawa;Takahiro Okabe;Einoshin Suzuki;Yoichi Sato
描述人物图像的颜色和纹理信息是人物再识别的关键之一。在本文中，我们提出了一种新的描述符的基础上的分层分布的像素特征。分层协方差描述子已成功地应用于图像分类。然而，像素特征的均值信息，这是缺乏的协方差，往往是主要的个人图像的判别信息。为了解决这个问题，我们描述了一个局部区域的图像通过分层高斯分布，其中的均值和协方差都包含在其参数。更具体地说，我们将该区域建模为一组多个高斯分布，其中每个高斯分布代表局部补丁的外观。高斯分布的集合的特征再次由另一个高斯分布描述。在这两个步骤中，与分层协方差描述符不同，所提出的描述符可以正确地对像素特征的均值和协方差信息进行建模。在五个数据库上进行的实验结果表明，该描述符表现出显着的高性能，优于国家的最先进的描述符的人重新识别。
Describing the color and textural information of a person image is one of the most crucial aspects of person re-identification. In this paper, we present a novel descriptor based on a hierarchical distribution of pixel features. A hierarchical covariance descriptor has been successfully applied for image classification. However, the mean information of pixel features, which is absent in covariance, tends to be major discriminative information of person images. To solve this problem, we describe a local region in an image via hierarchical Gaussian distribution in which both means and covariances are included in their parameters. More specifically, we model the region as a set of multiple Gaussian distributions in which each Gaussian represents the appearance of a local patch. The characteristics of the set of Gaussians are again described by another Gaussian distribution. In both steps, unlike the hierarchical covariance descriptor, the proposed descriptor can model both the mean and the covariance information of pixel features properly. The results of experiments conducted on five databases indicate that the proposed descriptor exhibits remarkably high performance which outperforms the state-of-the-art descriptors for person re-identification.