Fire image features selection and recognition based on rough set
HU Yan1,2*, WANG Huiqin1,2, QIN Weiwei2, ZOU Ting2, LIANG Junshan2
1. School of Management, Xi'an University of Architecture and Technology, Xi'an Shaanxi 710055, China;
2. School of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an Shaanxi 710055, China
Abstract：Concerning the contradiction of accuracy and real-time in image fire detection, a fire image features selection and recognition algorithm based on rough set was proposed. Firstly, through in-depth study on the flame image features, the top edge of flame driven by the combustion energy is very irregular, and obvious vibration phenomenon occurres. But the lower edge is the opposite. Based on this feature, the upper and lower edges of the jitter projection ratio can be used as a flame from the edge shape regular interference. Then, the six striking flame features were chosen in order to create training samples. When fire classification ability was not affected, the feature classification table gained by experiment was used to reduce attributes of the training samples. And the reduced information systems attributes were applied to train a support vector machine model, and the fire detection was realized. Finally, this fire detection algorithm was compared to the traditional Support Vector Machine (SVM) fire detection algorithm. The results show that the presented algorithm reduces redundant attributes, eliminates the dimension of fire image features space, and decreases the data of training and testing in classifier in case rough set as a SVM classifier prefix system. While ensuring recognition accuracy, the algorithm improves fire detection speed.
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HU Yan WANG Huiqin QIN Weiwei ZOU Ting LIANG Junshan. Fire image features selection and recognition based on rough set. Journal of Computer Applications, 2013, 33(03): 704-707.
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2013, Vol. 33 
