Center-biased machine learning techniques to determine saliency in digital images
US11663463B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 10, 2019 |
| Grant date | May 30, 2023 |
| Priority date | — |
| Expiry date | Jan 18, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V30/274
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A location-sensitive saliency prediction neural network generates location-sensitive saliency data for an image. The location-sensitive saliency prediction neural network includes, at least, a filter module, an inception module, and a location-bias module. The filter module extracts visual features at multiple contextual levels, and generates a feature map of the image. The inception module generates a multi-scale semantic structure, based on multiple scales of semantic content depicted in the image. In some cases, the inception block performs parallel analysis of the feature map, such as by parallel multiple layers, to determine the multiple scales of semantic content. The location-bias module generates a location-sensitive saliency map of location-dependent context of the image based on the multi-scale semantic structure and on a bias map. In some cases, the bias map indicates location-specific weights for one or more regions of the image.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.