基于局部特征过滤的快速火焰图像识别方法  

Fast flame recognition approach based on local feature filtering

作　　者：毛文涛[1,2] 王文朋[1] 蒋梦雪 欧阳军[3] 

机构地区：[1]河南师范大学计算机与信息工程学院,河南新乡453007 [2]河南省高校计算智能与数据挖掘工程技术中心,河南新乡453007 [3]首安工业消防有限公司,北京100010

出　　处：《计算机应用》2016年第10期2907-2911,共5页journal of Computer Applications

基　　金：国家自然科学基金资助项目(U1204609);河南省高校科技创新人才支持计划项目(15HASTIT022);河南省高校青年骨干教师资助计划项目(2014GGJS-046);河南省高等学校重点科研项目计划项目(15A520078);河南师范大学优秀青年科学基金资助项目(14YQ007)~~

摘　　要：传统的基于物理信号的火焰识别方法易被外部环境干扰,且现有火焰图像特征提取方法对于火焰和场景的区分度较低,从而导致火焰种类或场景改变时识别精度降低。针对这一问题,提出一种基于局部特征过滤和极限学习机的快速火焰识别方法,将颜色空间信息引入尺度不变特征变换(SIFT)算法。首先,将视频文件转化成帧图像,利用SIFT算法对所有图像提取特征描述符;其次,通过火焰在颜色空间上的信息特性进一步过滤局部噪声特征点,并借助关键点词袋(BOK)方法,将特征描述符转换成对应的特征向量;最后放入极限学习机进行训练,从而快速得到火焰识别模型。在火焰公开数据集及真实火灾场景图像进行的实验结果表明:所提方法对不同场景和火焰类型均具有较高的识别率和较快的检测速度,实验识别精度达97%以上;对于包含4 301张图片数据的测试集,模型识别时间仅需2.19 s;与基于信息熵、纹理特征、火焰蔓延率的支持向量机模型,基于SIFT、火焰颜色空间特性的支持向量机模型,基于SIFT的极限学习机模型三种方法相比,所提方法在测试集精度、模型构建时间上均占有优势。For flame recognition problem, the traditional recognition methods based on physical signal are easily affected by the external environment. Meanwhile, most of the current methods based on feature extraction of flame image are less discriminative to different scene and flame type, and then have lower recognition precision if the flame scene and type change. To overcome this drawback, a new fast recognition method for flame image was proposed by introducing colorspace information into Scale Invariant Feature Transform （SIFT） algorithm. Firstly, the feature descriptors of flame were extracted by SIFT algorithm from the frame images which were obtained from flame video. Secondly, the local noisy feature points were filtered by introducing the feature information of flame colorspace, and the feature descriptors were transformed into feature vectors by means of Bag Of Keypoints （BOK）. Finally, Extreme Learning Machine （ELM） was utilized to establish a fast flame recognition model. Experiments were conducted on open flame datasets and real-life flame images. The results show that for different flame scenes and types the accuracy of the proposed method is more than 97%, and the recognition time is just 2.19 s for test set which contains 4301 images. In addition, comparing with the other three methods such as support vector machine based on entropy, texture and flame spread rate, support vector machine based on SIFT and fire specialty in color space, ELM based on SIFT and fire specialty in color space, the proposed method outperforms in terms of recognition accuracy and speed.

关 键 词：火焰识别 特征提取 尺度不变特征变换 极限学习机 关键点词袋 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]