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Constrained Dictionary Learning and Probabilistic Hypergraph Ranking for Person Re-identification

机译：约束字典学习和概率超图排名用于人员重新识别

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摘要

Person re-identification is a fundamental and inevitable task in public security. In this paper, we propose a novel framework to improve the performance of this task. First, two different types of descriptors are extracted to represent a pedestrian: (1) appearance-based superpixel features, which are constituted mainly by conventional color features and extracted from the supepixel rather than a whole picture and (2) due to the limitation of discrimination of appearance features, the deep features extracted by feature fusion Network are also used. Second, a view invariant subspace is learned by dictionary learning constrained by the minimum negative sample (termed as DL-cMN) to reduce the noise in appearance-based superpixel feature domain. Then, we use deep features and sparse codes transformed by appearance-based features to establish the hyperedges respectively by k-nearest neighbor, rather than jointing different features simply. Finally, a final ranking is performed by probabilistic hypergraph ranking algorithm. Extensive experiments on three challenging datasets (VIPeR, PRID450S and CUHK01) demonstrate the advantages and effectiveness of our proposed algorithm.
机译：人员重新识别是公共安全中的一项基本和不可避免的任务。在本文中，我们提出了一个新颖的框架来改善此任务的性能。首先，提取两种不同类型的描述符来表示行人：（1）基于外观的超像素特征，主要由常规颜色特征构成，并从超像素而不是整个图片中提取;（2）由于图像的局限性识别外观特征时，还使用了通过特征融合网络提取的深层特征。第二，通过字典学习来学习视图不变子空间，该字典学习受到最小负样本（称为DL-cMN）的约束，以减少基于外观的超像素特征域中的噪声。然后，我们使用由基于外观的特征转换的深度特征和稀疏代码分别通过k最近邻来建立超边缘，而不是简单地将不同特征联合起来。最后，通过概率超图排名算法执行最终排名。在三个具有挑战性的数据集（VIPeR，PRID450S和CUHK01）上进行的大量实验证明了我们提出的算法的优势和有效性。

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