Embedding the multiple instance problem: applications to landmine detection with ground penetrating radar

Jeremy Bolton; Paul Gader; Hichem Frigui

doi:10.1117/12.2019027

7 June 2013 Embedding the multiple instance problem: applications to landmine detection with ground penetrating radar

Jeremy Bolton, Paul Gader, Hichem Frigui

Proceedings Volume 8709, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII; 87091Q (2013) https://doi.org/10.1117/12.2019027
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

Abstract

Multiple Instance Learning is a recently researched learning paradigm in machine intelligence which operates under conditions of uncertainty with the cost of increased computational burden. This increase in computational burden can be avoided by embedding these so-called multiple instances using a kernel function or other embedding function. In the following, a family of fast multiple instance relevance vector machines are used to learn and classify landmine signatures in ground penetrating radar data. Results indicate a significant reduction in computational complexity without a loss in classification accuracy in operating conditions.

Citation Download Citation

Jeremy Bolton, Paul Gader, and Hichem Frigui "Embedding the multiple instance problem: applications to landmine detection with ground penetrating radar", Proc. SPIE 8709, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII, 87091Q (7 June 2013); https://doi.org/10.1117/12.2019027

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