LANDMINE DETECTION WITH MULTIPLE INSTANCE HIDDEN MARKOV MODELS


Yuksel S. E. , Bolton J., Gader P. D.

22nd IEEE International Workshop on Machine Learning for Signal Processing (MLSP), Santander, Spain, 23 - 26 September 2012 identifier

  • Publication Type: Conference Paper / Full Text
  • City: Santander
  • Country: Spain

Abstract

A novel Multiple Instance Hidden Markov Model (MI-HMM) is introduced for classification of ambiguous time-series data, and its training is accomplished via Metropolis-Hastings sampling. 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 Learning (MIL) framework. The efficacy of the model is shown on a real landmine dataset. Experiments on the landmine dataset show that MI-HMM learning is very effective, and outperforms the state-of-the-art models that are currently being used in the field for landmine detection.