A new PSO-based approach to fire flame detection using K-Medoids clustering. (February 2017)

• Record Type: Journal Article
• Authors: Khatami, Amin; Mirghasemi, Saeed; Khosravi, Abbas; Lim, Chee Peng; Nahavandi, Saeid
• Is Part Of: Expert systems with applications. Volume 68(2017)
• Page Start: 69
• Page End: 80
• Publication Date: 2017-02
• Subjects: Fire detection -- Particle swarm optimisation -- K-medoids -- Otsu's thresholding method -- Contrast enhancement; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2016.09.021
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: 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. Abstract: 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.