Finding semantic parts in images
US9940577B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 7, 2015 |
| Grant date | Apr 10, 2018 |
| Priority date | — |
| Expiry date | Jun 4, 2036 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V40/10
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Embodiments of the present invention relate to finding semantic parts in images. In implementation, a convolutional neural network (CNN) is applied to a set of images to extract features for each image. Each feature is defined by a feature vector that enables a subset of the set of images to be clustered in accordance with a similarity between feature vectors. Normalized cuts may be utilized to help preserve pose within each cluster. The images in the cluster are aligned and part proposals are generated by sampling various regions in various sizes across the aligned images. To determine which part proposal corresponds to a semantic part, a classifier is trained for each part proposal and semantic part to determine which part proposal best fits the correlation pattern given by the true semantic part. In this way, semantic parts in images can be identified without any previous part annotations.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.