| Title |
Deep metric learning for image retrieval in smart city development |
| ID_Doc |
45638 |
| Authors |
Liu, Q; Li, WH; Chen, ZY; Hua, B |
| Title |
Deep metric learning for image retrieval in smart city development |
| Year |
2021 |
| Published |
|
| DOI |
10.1016/j.scs.2021.103067 |
| Abstract |
Deep metric learning (DML) aims to learn a consistent distance embedding where an anchor is closer within the same category than others. It underpins a variety of essential and significant tasks in the development of smart city including face recognition, landmark retrieval, pedestrian detection, person/vehicle re-identification, and so on. Traditional pair-based DML methods try to make full use of the data-to-data relations within a (mini-)batch, but they cannot grasp the data distribution information due to the batch size limitation. On the other hand, proxy-based DML schemes use different proxies to approximate the data distribution. However, the proxies are too sample to represent the intra-category variance. In this paper, we propose a simple but effective method, named soft-instance-label proxy, for embedding learning. It can capture the globe data distribution information while depicting the detailed intra-class data structure. The state-of-the-art empirical results on three public image retrieval benchmarks and two backbone networks demonstrate the superiority of our proposed method. Our Softinstance-label proxy method can have a Recall@1 improvement of 2.4% with Googlenet, largely surpassing the current state-of-art-methods while demonstrating great potential in the development of smart city. |
| Author Keywords |
Smart city; Image retrieval; Deep metric learning |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000688574100001 |
| WoS Category |
Construction & Building Technology; Green & Sustainable Science & Technology; Energy & Fuels |
| Research Area |
Construction & Building Technology; Science & Technology - Other Topics; Energy & Fuels |
| PDF |
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