摘要
针对图像型火灾探测方法检测准确度和实时性间的矛盾,提出了基于粗糙集的火灾图像特征选择和识别算法。首先通过对火焰图像特征的深入研究发现,在燃烧能量的驱动下火焰的上边缘极不规则,出现明显的震动现象,而下边缘却恰恰相反;基于此特点,可利用上下边缘抖动投影个数比作为火焰区别于边缘形状较规则的干扰。然后,选择火焰的6个显著特征构造训练样本,在火灾分类能力不受影响的前提下,使用实验所得的特征量归类表对训练样本进行属性约简,并将约简后的信息系统属性训练支持向量机模型,实现火灾探测。最后与传统支持向量机火灾探测算法做了比较。实验结果表明:将粗糙集作为支持向量机分类器的前置系统,把粗糙集理论的属性约简引入到支持向量机中,可以大大消除样本集冗余属性,降低了火灾图像特征空间的维数,减少了分类器训练和检测数据,在保证识别精度的同时,提高了算法的速度和泛化能力。
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 ;〈mage features, the top edge of flame driven by the combustion energy is very irregular, and obvious vibration phenomenon oecurres. 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.
出处
《计算机应用》
CSCD
北大核心
2013年第3期704-707,共4页
journal of Computer Applications
基金
陕西省教育厅产业化项目(2011JG12)
榆林市科技计划项目
西安建筑科技大学青年科技基金资助项目(QN1125)
关键词
火灾图像特征
粗糙集
属性约简
支持向量机
火灾识别
fire image feature
Rough Set （RS）
Attribute Reduction （AR）
Support Vector Machine （SVM）
fire recognition
