Semi-supervised person re-identification using multi-view clustering
US11823050B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 8, 2022 |
| Grant date | Nov 21, 2023 |
| Priority date | — |
| Expiry date | Dec 8, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V40/10
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A semi-supervised model incorporates deep feature learning and pseudo label estimation into a unified framework. The deep feature learning can include multiple convolutional neural networks (CNNs). The CNNs can be trained on available training datasets, tuned using a small amount of labeled training samples, and stored as the original models. Features are then extracted for unlabeled training samples by utilizing the original models. Multi-view clustering is used to cluster features to generate pseudo labels. Then the original models are tuned by using an updated training set that includes labeled training samples and unlabeled training samples with pseudo labels. Iterations of multi-view clustering and tuning using an updated training set can continue until the updated training set is stable.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.