Multiple instance feature learning for landmine detection in ground-penetrating radar data

Jeremy Bolton; Paul Gader; Hichem Frigui

doi:10.1117/12.849322

29 April 2010 Multiple instance feature learning for landmine detection in ground-penetrating radar data

Jeremy Bolton, Paul Gader, Hichem Frigui

Proceedings Volume 7664, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV; 766428 (2010) https://doi.org/10.1117/12.849322
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

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.

Citation Download Citation

Jeremy Bolton, Paul Gader, and 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); https://doi.org/10.1117/12.849322

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available