Name
Title | ||
|---|---|---|
Deep triplet-group network by exploiting symmetric and asymmetric information for person reidentification. |
Abstract | ||
|---|---|---|
Deep metric learning is an effective method for person reidentification. In practice, impostor samples generally possess more discriminative information than other negative samples. Specifically, existing triplet-based deep-learning methods cannot effectively remove impostors, because they cannot consider congeners of impostor and it may produce new impostors when removing existing impostors. To utilize discriminative information in triplets and make impostor and its congeners more clustering, we design oversymmetric and over-asymmetric relationships and apply these two constraints to triplet and impostors' congeners to train our deep triplet-group network with original individual images rather than handcrafted features. Extensive experiments with five benchmark datasets demonstrate that our method outperforms the state-of-the-art methods with regards to the rank-N matching accuracy.(C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1117/1.JEI.27.3.033033 | JOURNAL OF ELECTRONIC IMAGING |
Keywords | Field | DocType |
person reidentification,deep metric learning,deep learning,impostor,symmetric and asymmetric information | Computer vision,Information asymmetry,Pattern recognition,Computer science,Artificial intelligence | Journal |
Volume | Issue | ISSN |
27 | 3 | 1017-9909 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
2 | ||