NDLI: Context-based classification via mixture of hidden Markov model experts with applications in landmine detection

Content Provider	IET Digital Library
Author	Yuksel, Seniha E. Gader, Paul D.
Abstract	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.
Starting Page	873
Ending Page	883
Page Count	11
ISSN	17519632
Volume Number	10
e-ISSN	17519640
Issue Number	Issue 8, Dec (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/10/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2016.0138
Journal	IET Computer Vision
Publisher Date	2016-06-15
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique Context-based Classification Data Classification Hidden Markov Model HMM Image Classification Image Recognition Image Sequence Landmine Detection Markov Processes ME Model Mixture of Expert Model Mixture of Hidden Markov Model Expert Model Single Probabilistic Model Variable Length Sequence
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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