Abstract
In order to improve the image fire detection rate in spacious buildings, regression analysis is used to find the inherent relationship between flame features. Then support vector machines (SVM) are used to finish recognition. First, suspected fire regions are detected depending on improved fire-colored pixel detection and hierarchical clustering method. Then, features are extracted and analyzed with regression method. Finally, features are applied to a two-class SVM classifier for fire region verification. The experimental results show that the new feature, area overlap, is efficient and the proposed algorithm has a lower positive false rate than the previous algorithm.
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Acknowledgments
This research is supported by grants from Specialized Research Fund for the Doctoral Program of Higher Education of China (SRFDP) (20126120110008), Natural Science Basic Research Plan in Shaanxi Province of China (2012JQ8021), Industrialization Program Funded by Shaanxi Provincial Education Department (2011JG12), and Scientific Research Program Funded by Shaanxi Provincial Education Department (11JK1048).
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Jia, Y., Wang, H. (2014). Image Fire Detection System Based on Feature Regression Analysis and Support Vector Machine. In: Wen, Z., Li, T. (eds) Practical Applications of Intelligent Systems. Advances in Intelligent Systems and Computing, vol 279. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54927-4_14
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DOI: https://doi.org/10.1007/978-3-642-54927-4_14
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