Deep Multi-Index Hashing for Person Re-Identification
arXiv:1905.10980
Abstract
Traditional person re-identification (ReID) methods typically represent person images as real-valued features, which makes ReID inefficient when the gallery set is extremely large. Recently, some hashing methods have been proposed to make ReID more efficient. However, these hashing methods will deteriorate the accuracy in general, and the efficiency of them is still not high enough. In this paper, we propose a novel hashing method, called deep multi-index hashing (DMIH), to improve both efficiency and accuracy for ReID. DMIH seamlessly integrates multi-index hashing and multi-branch based networks into the same framework. Furthermore, a novel block-wise multi-index hashing table construction approach and a search-aware multi-index (SAMI) loss are proposed in DMIH to improve the search efficiency. Experiments on three widely used datasets show that DMIH can outperform other state-of-the-art baselines, including both hashing methods and real-valued methods, in terms of both efficiency and accuracy.
arXiv comment10 pages, 6 figures, 2 tables
References in corpus (6)
- In Defense of the Triplet Loss for Person Re-Identification
- FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification
- Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking
- Circulant Binary Embedding
- A Revisit of Hashing Algorithms for Approximate Nearest Neighbor Search
- A Revisit on Deep Hashings for Large-scale Content Based Image Retrieval