一种多尺度时频纹理特征融合的场景分类算法  

Multi-scale time-frequency texture feature fusion algorithm for scene classification

作　　者：史静[1] 朱虹[1] 邢楠[1] 韩勇[1] 杜森[1] 

机构地区：[1]西安理工大学自动化与信息工程学院,西安710048

出　　处：《仪器仪表学报》2016年第10期2333-2339,共7页Chinese Journal of Scientific Instrument

基　　金：西安市科技计划(CXY1509(13))项目资助

摘　　要：场景分类目前是机器视觉领域的一个研究热点,为了解决该研究领域中分类特征的提取问题,提出了一种多尺度纹理描述子(MSTD)特征。首先,采用小波变换,获得图像在时频域上的多尺度纹理视觉全局特征信息,之后提取反映局部细节的局部二值模式(LBP)特征,在时频域上进行融合,生成多尺度纹理描述子特征,以此作为图像分类的依据,最后采用支持向量机(SVM)作为分类器进行场景分类。在4个标准数据集上进行测试,实验结果表明,该方法具有较高的分类正确率,对室外场景的分类正确率都在84%以上。所提出的分类方法充分考虑了全局特征和尺度信息,增强了单层特征的区分度,有效地改善了分类的精度。Scene classification is currently a hot topic in the field of machine vision.In order to solve the problem of classification feature extraction,a multi-scale texture descriptor （Multi-scale Texture Descriptor,MSTD）characteristic is presented.Firstly,the wavelet transform is used to obtain a multi-scale global visual texture feature information in the time-frequency domain.The LBP feature reflecting local details and fusing in the time-frequency domain is extracted to generate multi-scale texture descriptors features.The final SVMis used as a classifier to classify the scene.Based on four standard data sets,experimental results show that the presented method has higher classification accuracy,and the outdoor scene classification accuracy rate is above 84%.The proposed classification method takes into account the global features and scale information to enhance the discrimination of single characteristics,which effectively improves the accuracy of the classification.

关 键 词：场景分类 多尺度 纹理特征 多尺度纹理描述子 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] TH79[自动化与计算机技术—计算机科学与技术]