Score fusion and training data recycling for video classification
US9147129B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 18, 2012 |
| Grant date | Sep 29, 2015 |
| Priority date | — |
| Expiry date | Dec 18, 2033 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V20/41
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Multiple classifiers can be applied independently to evaluate images or video. Where there are heavily imbalanced class distributions, a local expert forest model for meta-level score fusion for event detection can be used. Performance variations of classifiers in different regions of a score space can be adapted. Multiple pairs of experts based on different partitions, or “trees,” can form a “forest,” balancing local adaptivity and over-fitting. Among ensemble learning methods, stacking with a meta-level classifier can be used to fuse an output of multiple base-level classifiers to generate a final score. A knowledge-transfer framework can reutilize the base-training data for learning the meta-level classifier. By recycling the knowledge obtained during a base-classifier-training stage, efficient use can be made of all available information, such as can be used to achieve better fusion and better overall performance.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.