Training DHMMs of mine and clutter to minimize landmine detection errors

Zhao, Yunxin; Gader, Paul D.; Chen, Ping; Zhang, Yue

doi:10.1109/tgrs.2003.811761

Cited by 23 publications

(2 citation statements)

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“…These feature vectors are used to train a standard ME model. MCE‐HMM: Minimum classification error HMM is a discriminative learning method that minimises the total misclassification error. It was introduced by Juang et al [18] and used in [33, 34] for landmine detection. The parameters of MCE‐HMM as they appear in [34] were set as follows: η = 1, γ = 8, θ = 0, ɛ = 0.1.…”

Section: Experimental Results With Landmine Datamentioning

confidence: 99%

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Context‐based classification via mixture of hidden Markov model experts with applications in landmine detection

Yüksel

Gader

2016

IET Computer Vision

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In many applications data classification may be hindered by the existence of multiple contexts that produce an input sample. To alleviate the problems associated with multiple contexts, context‐based classification is a process that uses different classifiers depending on a measure of the context. Context‐based classifiers offer the promise of increasing performance by allowing classifiers to become experts at classifying input samples of certain types, rather than trying to force single classifiers to perform well on all possible inputs. This study introduces a novel mixture of experts (ME) model, the mixture of hidden Markov model experts, for context‐based classification of samples that are variable length sequences; and derives the update equations for a single probabilistic model that to learn the experts and a gate that connects the experts. The model has a similar high‐level structure to the ME model but has the novelty that the gates and the experts are HMMs and the input data are sequences. Experimental results are presented on three datasets including one for landmine detection. Detailed analysis of the model is provided; which, over multiple runs and cross‐validation experiments, show superior results over the compared algorithms.

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Section: Experimental Results With Landmine Datamentioning

confidence: 99%

“…It was introduced by Juang et al [18] and used in [33,34] for landmine detection. The parameters of MCE-HMM as they appear in [34] were set as follows: = 1, γ = 8, = 0, ɛ = 0.1. Classification rates are given in Table 2 in decreasing order.…”

Section: Landmine Detection Resultsmentioning

confidence: 99%