Multiple-Instance Hidden Markov Models With Applications to Landmine Detection

A novel multiple-instance hidden Markov model (MI-HMM) is introduced for classification of time-series data, and its training is developed using stochastic expectation maximization. The MI-HMM provides a single statistical form to learn the parameters of an HMM in a multiple-instance learning framework without introducing any additional parameters. The efficacy of the model is shown both on synthetic data and on a real landmine data set. Experiments on both the synthetic data and the landmine data set show that an MI-HMM can 1) achieve statistically significant performance gains when compared with the best existing HMM for the landmine detection problem, 2) eliminate the ad hoc approaches in training set selection, and 3) introduce a principled way to work with ambiguous time-series data.