Visual Cognition–Inspired Multi-View Vehicle Re-Identification via Laplacian-Regularized Correlative Sparse Ranking

Vehicle re-identification has gradually gained attention and widespread applications. However, most of the existing methods learn the discriminative features for identities by single-feature channel only. It is worth noting that visual cognition of the human eyes is a multi-channel system which usually seeks a sparse representation. Therefore, integrating the multi-view information in sparse representation is a natural way to boost computer vision tasks in challenging scenarios. In this paper, we propose to mine multi-view deep features via Laplacian-regularized correlative sparse ranking for vehicle re-identification. Specifically, first, we employ multiple baseline networks to generate features. Then, we explore the feature correlation via enforcing the correlation term into the multi-view Laplacian sparse ranking framework. The original rankings are obtained by the reconstruction coefficients between the probe and gallery. Finally, we utilize a re-ranking technique to further boost performance. Experimental results on public benchmark VeRi-776 and VehicleID datasets demonstrate that our approach outperforms state-of-the-art approaches. The Laplacian-regularized correlative sparse ranking as a general framework can be used in any multi-view feature fusion and will obtain more competitive results.