29 April 2010 Multiple instance feature learning for landmine detection in ground-penetrating radar data
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Abstract
Multiple instance learning (MIL) is a technique used for identifying a target pattern within sets of data. In MIL, a learner is presented with sets of samples; whereas in standard techniques, a learner is presented with individual samples. The MI scenario is encountered given the nature of landmine detection in GPR data, and therefore landmine detection results should benefit from the use of multiple instance techniques. Previously, a random set framework for multiple instance learning (RSF-MIL) was proposed which utilizes random sets and fuzzy measures to model the MIL problem. An improved version C-RSF-MIL was recently developed showing a increase in learning and classification performance. This new approach is used to learn and characterize features of landmines within GPR imagery for the purposes of classification. Experimental results show the benefits of using C-RSF-MIL for landmine detection in GPR imagery.
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Jeremy Bolton, Paul Gader, Hichem Frigui, "Multiple instance feature learning for landmine detection in ground-penetrating radar data", Proc. SPIE 7664, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV, 766428 (29 April 2010); doi: 10.1117/12.849322; https://doi.org/10.1117/12.849322
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KEYWORDS
Land mines

General packet radio service

Detection and tracking algorithms

Ground penetrating radar

Analytical research

Statistical modeling

Antennas

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