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. Attached Files Name Description MIMEType Size Downloads Title A new PSO-based approach to fire flame detection using K-Medoids clustering Author(s) Khatami, Amin Mirghasemi, Saeed Khosravi, Abbas orcid.org/0000-0001-6927-0744 Lim, Chee Peng orcid.org/0000-0003-4191-9083 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 K-medoids Otsu’s thresholding method Contrast enhancement Science & Technology Technology Computer Science, Artificial Intelligence Engineering, Electrical & Electronic Operations Research & Management Science Computer Science Engineering Otsu's thresholding method VIDEO SEQUENCES COMPUTER VISION COLOR MODEL NETWORKS SENSOR 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 Document type: Journal Article Collections: Centre for Intelligent Systems Research GTP Research Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Wed, 30 Nov 2016, 14:17:43 EST Thu, 01 Dec 2016, 04:01:52 EST Thu, 01 Dec 2016, 07:37:48 EST Wed, 11 Jan 2017, 09:52:37 EST Tue, 06 Jun 2017, 06:03:23 EST Wed, 07 Jun 2017, 12:36:40 EST Wed, 07 Jun 2017, 12:39:06 EST Filtered Full Citation counts:   Cited 17 times in TR Web of Science Cited 24 times in Scopus Search Google Scholar Access Statistics: 390 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Wed, 30 Nov 2016, 14:17:42 EST

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