Ensemble hidden Markov models with application to landmine detection

Abstract

We introduce an ensemble learning method for temporal data that uses a mixture of hidden Markov models (HMM). We hypothesize that the data are generated by K models, each of which reflects a particular trend in the data. The proposed approach, called ensemble HMM (eHMM), is based on clustering within the log-likelihood space and has two main steps. First, one HMM is fit to each of the N individual training sequences. For each fitted model, we evaluate the log-likelihood of each sequence. This results in an N-by-N log-likelihood distance matrix that will be partitioned into K groups using a relational clustering algorithm. In the second step, we learn the parameters of one HMM per cluster. We propose using and optimizing various training approaches for the different K groups depending on their size and homogeneity. In particular, we investigate the maximum likelihood (ML), the minimum classification error (MCE), and the variational Bayesian (VB) training approaches. Finally, to test a new sequence, its likelihood is computed in all the models and a final confidence value is assigned by combining the models' outputs using an artificial neural network. We propose both discrete and continuous versions of the eHMM. Our approach was evaluated on a real-world application for landmine detection using ground-penetrating radar (GPR). Results show that both the continuous and discrete eHMM can identify meaningful and coherent HMM mixture components that describe different properties of the data. Each HMM mixture component models a group of data that share common attributes. These attributes are reflected in the mixture model's parameters. The results indicate that the proposed method outperforms the baseline HMM that uses one model for each class in the data.

A Comprehensive Review of Conventional and Deep Learning Approaches for Ground-Penetrating Radar Detection of Raw Data

“…ROC curve can reflect the discrimination ability of the model. In 2015, Anis Hamdi et al proposed a novel ensemble HMM classification method that is based on clustering sequences in the log-likelihood space [27]. The eHMM uses multiple HMM models and fuses them for final decision making.…”

On Choosing Training and Testing Data for Supervised Algorithms in Ground-Penetrating Radar Data for Buried Threat Detection

“…We used two classifiers in this work: a radial basis function SVM [53] and a random forest (RF) classifier (100 trees, 2 variable splits at nodes, with central axis projection) [54]. These two classifiers were chosen because of their recent application to GPR resulting in state-of-the-art detection performance [6], [8], [12], [36], [45], [55], [56].…”

“…This metric is obtained by computing the area under the ROC curve between two false alarm rate (FAR) values (e.g., 0 and 0.005 FAR). pAUC is frequently used for performance comparisons in the BTD literature [33], [37], [55], [57], [58].…”

Gauss Gradient and SURF Features for Landmine Detection from GPR Images

“…Hamdi and Figui [6] presented a GPR landmine detection technique based on an ensemble hidden Markov model. They proved that the different textures of landmine activities are reflected in their model parameters.…”