Article: Unsupervised learning of local features for person re-identification with loss function Journal: International Journal of Autonomous and Adaptive Communications Systems (IJAACS) 2023 Vol.16 No.6 pp.536 - 551 Abstract: Many methods for person re-identification focus on making full use of local features, which typically requires either comprehensive manual labelling or complex pretreatment. This paper proposes a novel loss function, termed feature channels dropout and de-similarity loss that drives the autonomous learning of discriminative local features in convolutional neural networks. The proposed loss function consists of two components. The first is a feature channels dropout component designed to compel each feature channel to be discriminative. A novel channel-dropout function and a cross-channel-element-max function are applied in this component. The second component is a de-similarity component that uses the Pearson correlation coefficient to constrain feature channels and ensure they differ from each other. This component is conducive to diverse local features in mining. Extensive experiments on three large-scale re-identification datasets demonstrate that feature channels dropout and de-similarity loss achieve superior performance compared with state-of-the-art methods. Inderscience Publishers - linking academia, business and industry through research

Title: Unsupervised learning of local features for person re-identification with loss function

Authors: Lunzheng Tan; Guoluan Chen; Rui Ding; Limin Xia

Addresses: School of Information Engineering, Zhongshan Polytechnic, Zhongshan, Guangdong, 528400, China ' School of Information Engineering, Zhongshan Polytechnic, Zhongshan, Guangdong, 528400, China ' School of Optoelectronic Information, Zhongshan Torch Polytechnic, Zhongshan, Guangdong, 528436, China ' College of Information Science and Engineering, Central South University, Changsha, Hunan, 410075, China

Abstract: Many methods for person re-identification focus on making full use of local features, which typically requires either comprehensive manual labelling or complex pretreatment. This paper proposes a novel loss function, termed feature channels dropout and de-similarity loss that drives the autonomous learning of discriminative local features in convolutional neural networks. The proposed loss function consists of two components. The first is a feature channels dropout component designed to compel each feature channel to be discriminative. A novel channel-dropout function and a cross-channel-element-max function are applied in this component. The second component is a de-similarity component that uses the Pearson correlation coefficient to constrain feature channels and ensure they differ from each other. This component is conducive to diverse local features in mining. Extensive experiments on three large-scale re-identification datasets demonstrate that feature channels dropout and de-similarity loss achieve superior performance compared with state-of-the-art methods.

Keywords: person re-identification; local feature; unsupervised learning; loss function; CNN; FCDD-loss.

DOI: 10.1504/IJAACS.2023.134829

International Journal of Autonomous and Adaptive Communications Systems, 2023 Vol.16 No.6, pp.536 - 551

Received: 09 Aug 2021
Accepted: 10 Oct 2021
Published online: 14 Nov 2023 *

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Title: Unsupervised learning of local features for person re-identification with loss function

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