Automatic image annotation using semantic distance learning
US7890512B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 11, 2008 |
| Grant date | Feb 15, 2011 |
| Priority date | — |
| Expiry date | Aug 28, 2029 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F16/51
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Images are automatically annotated using semantic distance learning. Training images are manually annotated and partitioned into semantic clusters. Semantic distance functions (SDFs) are learned for the clusters. The SDF for each cluster is used to compute semantic distance scores between a new image and each image in the cluster. The scores for each cluster are used to generate a ranking list which ranks each image in the cluster according to its semantic distance from the new image. An association probability is estimated for each cluster which specifies the probability of the new image being semantically associated with the cluster. Cluster-specific probabilistic annotations for the new image are generated from the manual annotations for the images in each cluster. The association probabilities and cluster-specific probabilistic annotations for all the clusters are used to generate final annotations for the new image.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.