Deep Local Binary Coding for Person Re-Identification by Delving into the Details
MM 2020 - Proceedings of the 28th ACM International Conference on Multimedia
Person re-identification (ReID) has recently received extensive research interests due to its diverse applications in multimedia analysis and computer vision. However, the majority of existing works focus on improving matching accuracy, while ignoring matching efficiency. In this work, we present a novel binary representation learning framework for efficient person ReID, namely Deep Local Binary Coding (DLBC). Different from existing deep binary ReID approaches, DLBC attempts to learn discriminative binary codes by explicitly interacting with local visual details. Specifically, DLBC first extracts a set of local features from spatially salient regions of pedestrian images. Subsequently, DLBC formulates a new binary-local semantic mutual information (BSMI) maximization term, based on which a self-lifting (SL) block is built to further exploit the semantic importance of local features. The BSMI term together with the SL block simultaneously enhances the dependency of binary codes on selected local features as well as their robustness to cross-view visual inconsistency. In addition, an efficient optimizing method is developed to train the proposed deep models with orthogonal and binary constraints. Extensive experiments reveal that DLBC significantly minimizes the accuracy gap between binary ReID methods and the state-of-the-art real-valued ones, whilst remarkably reducing query time and memory cost.
deep binary coding, person re-identification, semantic mutual information maximization
J. Chen et al., "Deep Local Binary Coding for Person Re-Identification by Delving into the Details," MM 2020 - Proceedings of the 28th ACM International Conference on Multimedia, pp. 3034 - 3043, Oct 2020.
The definitive version is available at https://doi.org/10.1145/3394171.3413979