Context Aware Information Delivery for Mobile Devices

Abstract

Delivering the right amount of information to the right person is vital on the tactical battlefield. With the increasing use of mobile devices by the military, delivering relevant information instantaneously to the warfighter becomes possible. However, large quantities of data are being generated constantly while the human processing power and communication channels are limited. Therefore, data must be processed so it can be evaluated against operational needs. This data is collected in multiple modalities include images, videos and field reports with multi-sensor data. Providing automated processing of unstructured information promises to effectively connect information processing with operational decision making, dramatically reducing the time needed to identify relevant information for mission planning and execution. We describe a multi-view learning technique that augments the feature set used by a classifier in one modality with entity relationships discovered in other modalities. To accommodate the limited computation power of field devices, mostly handhelds, the multi-view learning algorithm is low complexity. It applies to multiple modalities, leveraging many-to-many correspondences among different modalities. Experiments on image and text are presented in the paper which show more than 20% improvement over categorizing text or images independently. The categorized information is matched to the mission and task needs. Finally, relevant information needs to be transmitted via limited bandwidth negotiated from limited resources.