A New Color Space Based on K-Medoids Clustering for Fire Detection

Khatami, Amin; Mirghasemi, Saeed; Khosravi, Abbas; Nahavandi, Saeid

doi:10.1109/smc.2015.481

Cited by 10 publications

(8 citation statements)

References 15 publications

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“…This L*a*b component, then converted in the array, which reduce the two dimensional geometric information in one dimension. This a*b space information is then clustered using genetic algorithm [24]. Each cluster [15] is processed to find the color segment of the image.…”

Section: Experiments and Results Analysismentioning

confidence: 99%

An implementation of novel genetic based clustering algorithm for color image segmentation

Jaiswal

Sharma

Varma³

2019

TELKOMNIKA

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The color image segmentation is one of most crucial application in image processing. It can apply to medical image segmentation for a brain tumor and skin cancer detection or color object detection on CCTV traffic video image segmentation and also for face recognition, fingerprint recognition etc. The color image segmentation has faced the problem of multidimensionality. The color image is considered in five-dimensional problems, three dimensions in color (RGB) and two dimensions in geometry (luminosity layer and chromaticity layer). In this paper the, L*a*b color space conversion has been used to reduce the one dimensional and geometrically it converts in the array hence the further one dimension has been reduced. The a*b space is clustered using genetic algorithm process, which minimizes the overall distance of the cluster, which is randomly placed at the start of the segmentation process. The segmentation results of this method give clear segments based on the different color and it can be applied to any application.

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Section: Experiments and Results Analysismentioning

confidence: 99%

An implementation of novel genetic based clustering algorithm for color image segmentation

Jaiswal

Sharma

Varma³

2019

TELKOMNIKA

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“…13 Partition-based algorithms are widely used because of their low time complexity and high computing efficiency, and they are also suitable for use with large datasets. A number of partition-based algorithms have been proposed for image color clustering, 10,[16][17][18][19][20][21] including k-means and FCM. However, most of the partition-based algorithms require the number of clusters to be preconfigured manually, and the initial cluster centers for these algorithms are difficult to choose.…”

Section: Related Studymentioning

confidence: 99%

A Student's t‐based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering

Zheng

Cao

et al. 2020

Color Research & Application

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Image color clustering is a basic technique in image processing and computer vision, which is often applied in image segmentation, color transfer, contrast enhancement, object detection, skin color capture, and so forth. Various clustering algorithms have been employed for image color clustering in recent years. However, most of the algorithms require a large amount of memory or a predetermined number of clusters. In addition, some of the existing algorithms are sensitive to the parameter configurations. In order to tackle the above problems, we propose an image color clustering method named Student's t‐based density peaks clustering with superpixel segmentation (tDPCSS), which can automatically obtain clustering results, without requiring a large amount of memory, and is not dependent on the parameters of the algorithm or the number of clusters. In tDPCSS, superpixels are obtained based on automatic and constrained simple non‐iterative clustering, to automatically decrease the image data volume. A Student's t kernel function and a cluster center selection method are adopted to eliminate the dependence of the density peak clustering on parameters and the number of clusters, respectively. The experiments undertaken in this study confirmed that the proposed approach outperforms k‐means, fuzzy c‐means, mean‐shift clustering, and density peak clustering with superpixel segmentation in the accuracy of the cluster centers and the validity of the clustering results.

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“…The fire-flame color is used as the first marker in a significant amount of published studies. Even if new color space was published by Khatami et al [3,4], many algorithms use simplified color rules. Their work [3,4] was aimed to develop a method for fire clustering by color.…”

Section: Color Detectionmentioning

confidence: 99%

“…Even if new color space was published by Khatami et al [3,4], many algorithms use simplified color rules. Their work [3,4] was aimed to develop a method for fire clustering by color. Swarm algorithms were used for defining coefficients of the convolutional matrix in order to select fire-like areas.…”

Section: Color Detectionmentioning

confidence: 99%

Fire Detection in Video Stream by Using Simple Artificial Neural Network

Janku

Oplatková

Dulík

et al. 2018

mendel

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This paper deals with the preliminary research of the fire detection in a video stream. Early fire detection can save lives and properties from huge losses and damages. Therefore the surveillance of the areas is necessary. Early fire discovery with high accuracy, i.e. a low number of false positive or false negative cases, is essential in any environment, especially in places with the high motion of people. The traditional fire detection sensors have some drawbacks: they need separate systems and infrastructure to be implemented, to use sensors in the case of the industrial environment with open fire technologies is often impossible, and others. The fire detection in a video stream is one of the possible and feasible solutions suitable for replacement or supplement of conventional fire detection sensors without a need for installation a huge infrastructure. The paper provides the state of the art in the fire detection. The following part of the paper proposes the new system of feature extraction and describes the feedforward neural network which was used for the training and testing of the proposed idea. The promising results are presented with over 93% accuracy on a selected dataset of movies which consist of more and highly varied instances than published by other researchers involved in the fire detection field. The structure of the neural networks promises higher computational speed than currently implemented deep learning systems.

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A New Color Space Based on K-Medoids Clustering for Fire Detection

Cited by 10 publications

References 15 publications

An implementation of novel genetic based clustering algorithm for color image segmentation

An implementation of novel genetic based clustering algorithm for color image segmentation

A Student's t‐based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering

Fire Detection in Video Stream by Using Simple Artificial Neural Network

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