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

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

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作者：Amin Khatami ; ^a ; ^{skhatami@deakin.edu.au" class="auth_mail" title="E-mail the corresponding author} ; Saeed Mirghasemi^b ; ^{saeed.mirghasemi@ecs.vuw.ac.nz" class="auth_mail" title="E-mail the corresponding author} ; Abbas Khosravi^a ; ^{abbas.khosravi@deakin.edu.au" class="auth_mail" title="E-mail the corresponding author} ; Chee Peng Lim^a ; ^{chee.lim@deakin.edu.au" class="auth_mail" title="E-mail the corresponding author} ; Saeid Nahavandi^a ; ^{saeid.nahavandi@deakin.edu.au" class="auth_mail" title="E-mail the corresponding author}
关键词：Fire detection ; Particle swarm optimisation ; K-medoids ; Otsu&rsquo ; s thresholding method ; Contrast enhancement
刊名：Expert Systems with Applications
出版年：2017
出版时间：February 2017
年：2017
卷：68
期：Complete
页码：69-80
全文大小：4349 K

文摘

•: Colour space with a linear multiplication of a conversion matrix and colour features.
•: A contrast enhancement method is performed on RGB images before conversion.
•: PSO and sample pixels obtain weights of the colour-differentiator conversion matrix.
•: K-medoids provides a fitness metric for the PSO procedure.

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