Video concept detection using multi-layer multi-instance learning
US8804005B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 29, 2008 |
| Grant date | Aug 12, 2014 |
| Priority date | — |
| Expiry date | Dec 19, 2031 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V20/40
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Visual concepts contained within a video clip are classified based upon a set of target concepts. The clip is segmented into shots and a multi-layer multi-instance (MLMI) structured metadata representation of each shot is constructed. A set of pre-generated trained models of the target concepts is validated using a set of training shots. An MLMI kernel is recursively generated which models the MLMI structured metadata representation of each shot by comparing prescribed pairs of shots. The MLMI kernel is subsequently utilized to generate a learned objective decision function which learns a classifier for determining if a particular shot (that is not in the set of training shots) contains instances of the target concepts. A regularization framework can also be utilized in conjunction with the MLMI kernel to generate modified learned objective decision functions. The regularization framework introduces explicit constraints which serve to maximize the precision of the classifier.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.