Push for Center Learning via Orthogonalization and Subspace Masking for Person Re-Identification

TL;DR

This paper introduces a novel orthogonal center learning approach with subspace masking and pooling strategies to improve person re-identification accuracy across multiple large-scale datasets.

Contribution

It proposes a new center learning module with orthogonalization, a subspace masking mechanism, and an integrated pooling method to enhance feature discrimination and generalization.

Findings

Outperforms state-of-the-art on Market-1501, DukeMTMC-ReID, CUHK03, MSMT17 datasets.

Achieves higher accuracy and robustness in person re-identification.

Demonstrates the effectiveness of orthogonalization and subspace masking in feature learning.

Abstract

Person re-identification aims to identify whether pairs of images belong to the same person or not. This problem is challenging due to large differences in camera views, lighting and background. One of the mainstream in learning CNN features is to design loss functions which reinforce both the class separation and intra-class compactness. In this paper, we propose a novel Orthogonal Center Learning method with Subspace Masking for person re-identification. We make the following contributions: (i) we develop a center learning module to learn the class centers by simultaneously reducing the intra-class differences and inter-class correlations by orthogonalization; (ii) we introduce a subspace masking mechanism to enhance the generalization of the learned class centers; and (iii) we devise to integrate the average pooling and max pooling in a regularizing manner that fully exploits their powers. Extensive experiments show that our proposed method consistently outperforms the state-of-the-art methods on the large-scale ReID datasets including Market-1501, DukeMTMC-ReID, CUHK03 and MSMT17.