In situ learning for underwater object recognition

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.