LANDMINE DETECTION WITH MULTIPLE INSTANCE HIDDEN MARKOV MODELS

Date

2012-09-26

Author

Yuksel, Seniha Esen
Bolton, Jeremy
Gader, Paul D.

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Time series data, Ground penetrating radar, Landmine detection, Metropolis-Hastings sampling, Hidden Markov models, Multiple instance learning

URI

https://hdl.handle.net/11511/66245

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Engineering, Conference / Seminar

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Citation Formats

S. E. Yuksel, J. Bolton, and P. D. Gader, “LANDMINE DETECTION WITH MULTIPLE INSTANCE HIDDEN MARKOV MODELS,” 2012, p. 0, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/66245.