Identifying anomalous object types during classification
US8270733B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Aug 31, 2009 |
| Grant date | Sep 18, 2012 |
| Priority date | — |
| Expiry date | Dec 9, 2030 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V10/7715
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques are disclosed for identifying anomaly object types during classification of foreground objects extracted from image data. A self-organizing map and adaptive resonance theory (SOM-ART) network is used to discover object type clusters and classify objects depicted in the image data based on pixel-level micro-features that are extracted from the image data. Importantly, the discovery of the object type clusters is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. The SOM-ART network is adaptive and able to learn while discovering the object type clusters and classifying objects and identifying anomaly object types.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.