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Lookup NU author(s): Dr Wanqing ZhaoORCiD
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© Springer-Verlag Berlin Heidelberg 2014.In this paper, a systematic approach adopting sparse leastsquares SVMs (LS-SVMs) is proposed to automatically detect fire using vision-based systems with fast speed and good performance. Within this framework, the features are first extracted from input images using wavelet analysis. The LS-SVM is then trained on the obtained dataset with global support vectors (GSVs) selected by a fast subset selection method, in the end of which the classifier parameters can be directly calculated rather than updated during the training process, leading to a significant saving of computing time. This sparse classifier only depends on the GSVs rather than all the patterns, which helps to reduce the complexity of the classifier and improve the generalization performance. Detection results on real fire images show the effectiveness and efficiency of the proposed approach.
Author(s): Zhang J, Li K, Zhao W, Fei M, Wang Y
Editor(s): Fei M; Peng C; Su Z; Song Y; Han Q
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: International Conference on Life System Modeling and Simulation, LSMS 2014 and International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2014
Year of Conference: 2014
Pages: 351-362
Print publication date: 09/10/2014
Acceptance date: 01/01/1900
Publisher: Springer Verlag
URL: https://doi.org/10.1007/978-3-662-45261-5_37
DOI: 10.1007/978-3-662-45261-5_37
Library holdings: Search Newcastle University Library for this item
Series Title: Communications in Computer and Information Science
ISBN: 9783662452608
