In situ learning for underwater object recognition

Award Information
Agency:
Department of Defense
Branch
n/a
Amount:
$1,453,945.00
Award Year:
2010
Program:
SBIR
Phase:
Phase II
Contract:
N00014-10-C-0287
Award Id:
92533
Agency Tracking Number:
N091-066-0370
Solicitation Year:
2009
Solicitation Topic Code:
N091-066
Solicitation Number:
2009.1
Small Business Information
1009 Slater Rd., Suite 200, Durham, NC, -
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
147201342
Principal Investigator:
Patrick Rabenold
ENG 3
(919) 323-3452
prabenold@siginnovations.com
Business Contact:
Samantha Venters
VP Finance
(919) 323-3453
sventers@siginnovations.com
Research Institute:
n/a
Abstract
In the proposed Phase II program, the methods developed and implemented during Phase I research will be fully integrated within a common Bayesian in situ learning framework. We have developed several Bayesian classifiers, to which we will apply label acquisition and label confidence techniques. Additionally, we will extend the in situ learning framework to include multi-task learning. Previously collected sensing data are often available from different sensors or environments. Not all data are related, however the potential exists to share information between related tasks and exploit the contextual information of previous tasks. The current in situ learning process is inherently myopic; the algorithm identifies the single most-informative data sample. The ability to select multiple samples without relearning the classifier can increase computational efficiency and maximize analyst workload. Based on the theory of submodular functions, non-myopic in situ learning techniques for subset selection will be developed and integrated into the Bayesian framework. Finally, new statistical embedding technology will be investigated that allows an analyst to synthesize data for training and to augment the label acquisition process. A low-dimensional embedded space may be visualized, and any location on the manifold can be recreated in the original high-dimensional space.

* information listed above is at the time of submission.

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