A new color space based on K-medoids clustering for fire detection

Khatami, Amin, Mirghasemi, Saeed, Khosravi, Abbas and Nahavandi, Saeid 2015, A new color space based on K-medoids clustering for fire detection, in SMC 2015 : Proceedings of 2015 IEEE International Conference on Systems, Man and Cybernetics, IEEE, Piscataway, N.J., pp. 2755-2760, doi: 10.1109/SMC.2015.481.

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Title A new color space based on K-medoids clustering for fire detection
Author(s) Khatami, Amin
Mirghasemi, Saeed
Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Conference name IEEE International Conference on Systems, Man, and Cybernetics (2015 : Hong Kong, China)
Conference location Hong Kong, China
Conference dates 9-12 Oct. 2015
Title of proceedings SMC 2015 : Proceedings of 2015 IEEE International Conference on Systems, Man and Cybernetics
Publication date 2015
Series IEEE International Conference on Systems Man and Cybernetics Conference Proceedings
Start page 2755
End page 2760
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) Science & Technology
Computer Science, Cybernetics
Computer Science, Information Systems
Computer Science, Theory & Methods
Computer Science
fire detection
particle Swarm Optimization
color segmentation
Summary Pixel color has proven to be a useful and robust cue for detection of most objects of interest like fire. In this paper, a hybrid intelligent algorithm is proposed to detect fire pixels in the background of an image. The proposed algorithm is introduced by the combination of a computational search method based on a swarm intelligence technique and the Kemdoids clustering method in order to form a Fire-based Color Space (FCS), in fact, the new technique converts RGB color system to FCS through a 3*3 matrix. This algorithm consists of five main stages:(1) extracting fire and non-fire pixels manually from the original image. (2) using K-medoids clustering to find a Cost function to minimize the error value. (3) applying Particle Swarm Optimization (PSO) to search and find the best W components in order to minimize the fitness function. (4) reporting the best matrix including feature weights, and utilizing this matrix to convert the all original images in the database to the new color space. (5) using Otsu threshold technique to binarize the final images. As compared with some state-of-the-art techniques, the experimental results show the ability and efficiency of the new method to detect fire pixels in color images.
ISSN 1062-922X
Language eng
DOI 10.1109/SMC.2015.481
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30081788

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