Method and device for Quasi-Gibbs structure sampling by deep permutation for person identity inference
US10339408B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 22, 2016 |
| Grant date | Jul 2, 2019 |
| Priority date | — |
| Expiry date | Dec 15, 2037 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V20/52
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present disclosure provides a method and device for visual appearance based person identity inference. The method may include obtaining a plurality of input images. The input images include a gallery set of images containing, persons-of-interest and a probe set of images containing person detections, and one input image corresponds to one person. The method may further include extracting N feature maps from the input images using a Deep Neural Network, N being a natural number; constructing N structure samples of the N feature maps using conditional random field (CRF) graphical models; learning the N structure samples from an implicit common latent feature space embedded in the N structure samples; and according to the learned structures, identifying one or more images from the probe set containing a same person-of-interest as an image in the gallery set.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.