Determining fine-grain visual style similarities for digital images by extracting style embeddings disentangled from image content
US11709885B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 18, 2020 |
| Grant date | Jul 25, 2023 |
| Priority date | — |
| Expiry date | Apr 21, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V20/30
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and flexibly identifying digital images with similar style to a query digital image using fine-grain style determination via weakly supervised style extraction neural networks. For example, the disclosed systems can extract a style embedding from a query digital image using a style extraction neural network such as a novel two-branch autoencoder architecture or a weakly supervised discriminative neural network. The disclosed systems can generate a combined style embedding by combining complementary style embeddings from different style extraction neural networks. Moreover, the disclosed systems can search a repository of digital images to identify digital images with similar style to the query digital image. The disclosed systems can also learn parameters for one or more style extraction neural network through weakly supervised training without a specifically labeled style ontology for sample digital images.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.