A new PSO-based approach to fire flame detection using K-Medoids clustering

Khatami, Amin, Mirghasemi, Saeed, Khosravi, Abbas, Lim, Chee Peng and Nahavandi, Saeid 2017, A new PSO-based approach to fire flame detection using K-Medoids clustering, Expert systems with applications, vol. 68, pp. 69-80, doi: 10.1016/j.eswa.2016.09.021.

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Title A new PSO-based approach to fire flame detection using K-Medoids clustering
Author(s) Khatami, Amin
Mirghasemi, Saeed
Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Lim, Chee PengORCID iD for Lim, Chee Peng orcid.org/0000-0003-4191-9083
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Journal name Expert systems with applications
Volume number 68
Start page 69
End page 80
Total pages 12
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2017-02
ISSN 0957-4174
Keyword(s) Fire detection
Particle swarm optimisation
Otsu’s thresholding method
Contrast enhancement
Science & Technology
Computer Science, Artificial Intelligence
Engineering, Electrical & Electronic
Operations Research & Management Science
Computer Science
Otsu's thresholding method
Summary Automated computer vision-based fire detection has gained popularity in recent years, as every fire detection needs to be fast and accurate. In this paper, a new fire detection method using image processing techniques is proposed. We explore how to create a fire flame-based colour space via a linear multiplication of a conversion matrix and colour features of a sample image. We show how the matrix multiplication can result in a differentiating colour space, in which the fire part is highlighted and the non-fire part is dimmed. Particle Swarm Optimization (PSO) and sample pixels from an image are used to obtain the weights of the colour-differentiating conversion matrix, and K-medoids provides a fitness metric for the PSO procedure. The obtained conversion matrix can be used for fire detection on different fire images without performing the PSO procedure. This allows a fast and easy implementable fire detection system. The empirical results indicate that the proposed method provides both qualitatively and quantitatively better results when compared to some of the conventional and state-of-the-art algorithms.
Language eng
DOI 10.1016/j.eswa.2016.09.021
Field of Research 01 Mathematical Sciences
08 Information And Computing Sciences
Socio Economic Objective 970110 Expanding Knowledge in Technology
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2016, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30089715

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