Instance-weighted mixture modeling to enhance training collections for image annotation
US9646226B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 16, 2014 |
| Grant date | May 9, 2017 |
| Priority date | — |
| Expiry date | Jun 14, 2035 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F18/214
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Automatic selection of training images is enhanced using an instance-weighted mixture modeling framework called ARTEMIS. An optimization algorithm is derived that in addition to mixture parameter estimation learns instance-weights, essentially adapting to the noise associated with each example. The mechanism of hypothetical local mapping is evoked so that data in diverse mathematical forms or modalities can be cohesively treated as the system maintains tractability in optimization. Training examples are selected from top-ranked images of a likelihood-based image ranking. Experiments indicate that ARTEMIS exhibits higher resilience to noise than several baselines for large training data collection. The performance of ARTEMIS-trained image annotation system is comparable to using manually curated datasets.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.