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首页> 外文期刊>Pattern Recognition: The Journal of the Pattern Recognition Society >Consistency-Preserving deep hashing for fast person re-identification
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Consistency-Preserving deep hashing for fast person re-identification

机译：快速保留一致性保留深哈希识别

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摘要

Numerous methods have been proposed for person re-identification (Re-ID) with promising performances. While most of them neglect the matching efficiency which is crucial in real-world applications. Recently, several hashing based approaches have been developed, which consider the importance of matching speed in large-scale datasets. Despite the considerable efficiency of these traditional and deep learning based hashing methods, the concomitant matching accuracy reduction is unacceptable in practical application. Towards this end, we propose a novel deep hashing framework, namely Consistency-Preserving Deep Hashing (CPDH), aiming to bridge the gap between the effective high-dimensional feature and low-dimensional binary vector by focusing on the consistency preservation of hash code. First, CPDH designs a new hash structure to extract the hash code. Next, a noise consistency cost is proposed to improve robustness of both hash code and high-dimensional feature. Finally, a topology consistency cost is provided to maintain the ordinal relation between the high-dimensional feature space and Hamming space. Comprehensive experimental results on three widely-used benchmark datasets demonstrate the superior performance of proposed method as compared with existing state-of-the-art approaches. (C) 2019 Elsevier Ltd. All rights reserved.
机译：已经提出了具有有前途表演的人重新识别（RE-ID）的许多方法。虽然他们中的大多数忽视了真实应用中至关重要的匹配效率。最近，已经开发了几种基于散列的方法，这考虑了大规模数据集中匹配速度的重要性。尽管这些传统和深度学习的散列方法具有相当大的效率，但在实际应用中，伴随的匹配精度降低是不可接受的。为此，我们提出了一种新的深度散列框架，即一致性保留的深脊（CPDH），旨在通过专注于哈希码的一致性保存来弥合有效高维特征和低维二进制矢量之间的间隙。首先，CPDH设计一个新的哈希结构以提取散列码。接下来，提出了一种噪声一致性成本，以提高散列码和高维特征的鲁棒性。最后，提供了一种拓扑一致性成本，以维持高维特征空间与汉明空间之间的序数关系。三种广泛使用的基准数据集的综合实验结果表明，与现有最先进的方法相比，所提出的方法的卓越性能。 （c）2019年elestvier有限公司保留所有权利。

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