Journal
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
Volume 22, Issue -, Pages S7665-S7675Publisher
SPRINGER
DOI: 10.1007/s10586-018-2368-8
Keywords
Fire recognition; Feature extraction; SIFT feature; Incremental vector support vector machine; IV-SVM classifier
Funding
- National Natural Science Foundation of China [61702052]
- Science and Technology Service Platform of Hunan Province [2012TP1001]
- Open Research Fund of Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation [2015TP1005]
- Changsha Science and Technology Planning [KQ1703018, KQ1706064]
- Research Foundation of Education Bureau of Hunan Province [12C0010, 17A007]
- ZOOMLION Intelligent Technology Limited Company [2017zkhx130]
- Hunan Province Undergraduates Innovating Experimentation Project [(2016) 283-946]
- Teaching and Reforming Project of Changsha University of Science and Technology [JG1755]
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For existed problems on fire detection fields, the traditional recognition methods on fire usually based on sensor's signals are easily affected by the external environment elements. Meanwhile, most of the current methods based on feature extraction of fire image are less discriminative to different scene and fire type, and have lower recognition precision if the fire scene and type change. To overcome the drawback on fire recognition, the new fast recognition method for fire image has proposed by introducing color space information into Scale Invariant Feature Transform (SIFT) algorithm. Firstly, the feature descriptors of fire are extracted by SIFT algorithm from the fire images which are obtained from internet databases. Secondly, the local noisy feature points are filtered by introducing the feature information of fire color space. Thirdly, the feature descriptors are transformed into feature vectors, and then Incremental Vector Support Vector Machine classifier is utilized to establish the fast fire recognition model. The experiments are conducted on real-life fire image from internet. The experimental results had shown that for different fire scenes and types, the proposed algorithm has outperformed Kim's method, Dimitropoulos's method and Sumei's method in terms of recognition accuracy and algorithm's running speed. The proposed algorithm has better application prospects than Kim's method, Dimitropoulos's method and Sumei's method.
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