Multiple instance learning for hidden Markov models: application to landmine detection

Jeremy Bolton; Seniha Esen Yuksel; Paul Gader

doi:10.1117/12.2016489

7 June 2013 Multiple instance learning for hidden Markov models: application to landmine detection

Jeremy Bolton, Seniha Esen Yuksel, Paul Gader

Proceedings Volume 8709, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII; 87091M (2013) https://doi.org/10.1117/12.2016489
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. A Multiple Instance Hidden Markov Model (MI-HMM) is investigated with applications to landmine detection using ground penetrating radar data. Without introducing any additional parameters, the MI-HMM provides an elegant and simple way to learn the parameters of an HMM in a multiple instance framework. The efficacy of the model is shown on a real landmine dataset. Experiments on the landmine dataset show that MI-HMM learning is effective.

Citation Download Citation

Jeremy Bolton, Seniha Esen Yuksel, and Paul Gader "Multiple instance learning for hidden Markov models: application to landmine detection", Proc. SPIE 8709, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII, 87091M (7 June 2013); https://doi.org/10.1117/12.2016489

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