Real Time Burning Image Classification Using Support Vector Machine

T.S. Hai; L.M. Triet; L.H. Thai; N.T. Thuy

Research Article

Real Time Burning Image Classification Using Support Vector Machine

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@ARTICLE{10.4108/eai.6-7-2017.152760,
    author={T.S. Hai and L.M. Triet and L.H. Thai and N.T. Thuy},
    title={Real Time Burning Image Classification Using Support Vector Machine},
    journal={EAI Endorsed Transactions on Context-aware Systems and Applications},
    volume={4},
    number={12},
    publisher={EAI},
    journal_a={CASA},
    year={2017},
    month={7},
    keywords={burning image classification; Support Vector Machine (SVM); multi-colour channels.},
    doi={10.4108/eai.6-7-2017.152760}
}

T.S. Hai
L.M. Triet
L.H. Thai
N.T. Thuy
Year: 2017
Real Time Burning Image Classification Using Support Vector Machine
CASA
EAI
DOI: 10.4108/eai.6-7-2017.152760

T.S. Hai¹^,*, L.M. Triet², L.H. Thai³, N.T. Thuy⁴

1: 1st Informatics Technology Department, University of Pedagogy, HCMC, Vietnam
2: Informatics Technology Department, University of Pedagogy, HCMC, Vietnam
3: Computer Science Department, University of Science, HCMC, Vietnam
4: University of Engineering and Technology, Ha Noi, Vietnam

*Contact email: haits@hcmup.edu.vn

Abstract

Burning image classification is critical and attempted problems in medical image processing. This paper has proposed the real time image classification for burning image to automatically identify the degrees of burns in three levels: II, III, and IV. The proposed model uses the multi-colour channels extraction and binary based on adaptive threshold. The proposed model uses One-class Support Vector Machine instead of traditional Support Vector Machine (SVM) because of unbalanced degrees of burns images database. The classifying precision 77.78% shows the feasibility of our proposed model.

Keywords: burning image classification; Support Vector Machine (SVM); multi-colour channels.

Received: 31st Aug 2016
Accepted: 21st May 2017
Published: 6th Jul 2017
Publisher: EAI

: http://dx.doi.org/10.4108/eai.6-7-2017.152760

Copyright © 2017 T.S. Hai et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.