Learned fusion operators based on matrix completion

Kelly K. D. Risko; Charles F. Hester

doi:10.1117/12.885011

6 June 2011 Learned fusion operators based on matrix completion

Kelly K. D. Risko, Charles F. Hester

Author Affiliations +

Proceedings Volume 8064, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2011; 80640K (2011) https://doi.org/10.1117/12.885011
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

Abstract

The efficient and timely management of imagery captured in the battlefield requires methods capable of searching the voluminous databases and extracting highly symbolic concepts. When processing images, a semantic and definition gap exists between machine representations and the user's language. Based on matrix completion techniques, we present a fusion operator that fuses imagery and expert knowledge provided by user inputs during post analysis. Specifically, an information matrix is formed from imagery and a class map as labeled by an expert. From this matrix an image operator is derived for the extraction/prediction of information from future imagery. We will present results using this technique on single mode data.

Citation Download Citation

Kelly K. D. Risko and Charles F. Hester "Learned fusion operators based on matrix completion", Proc. SPIE 8064, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2011, 80640K (6 June 2011); https://doi.org/10.1117/12.885011

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