Training a convolutional neural network for image retrieval with a listwise ranking loss function
US11521072B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 11, 2020 |
| Grant date | Dec 6, 2022 |
| Priority date | — |
| Expiry date | Jun 18, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/09
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method of performing image retrieval includes: obtaining a query image; generating a global feature descriptor of the query image by inputting the query image into a convolutional neural network (CNN) and obtaining the global feature descriptor as an output of the CNN, where parameters of the CNN are learned during training of the CNN on a batch of training images using a listwise ranking loss function and optimizing a quantized mean average precision ranking evaluation metric; determining similarities between the query image and other images based on distances between the global feature descriptor of the query image and global feature descriptors of the other images, respectively; ranking the other images based on the similarities, respectively; and selecting a set of the other images based on the similarities between the query image and the other images.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.