核协同表示下的多特征融合场景识别  

Scene Classification Based on Multi-Feature Fusion and Kernel Collaborative Representation

作　　者：宗海燕 吴秦 王田辰[1,2] 张淮 ZONG Haiyan;WU Qin;WANG Tianchen;ZHANG Huai(Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence,Jiangnan University,Wuxi,Jiangsu 214122,China;Engineering Research Center of Internet of Things Technology Applications Ministry of Education,Wuxi,Jiangsu 214122,China)

机构地区：[1]江南大学江苏省模式识别与计算智能工程实验室,江苏无锡214122 [2]物联网技术应用教育部工程研究中心,江苏无锡214122

出　　处：《计算机科学与探索》2019年第6期1038-1048,共11页Journal of Frontiers of Computer Science and Technology

基　　金：国家自然科学基金Nos.61202312,61673193;中央高校基本科研业务费专项资金Nos.JUSRP51635B,JUSRP51510~~

摘　　要：针对复杂场景识别中单一特征提供信息不充分这一问题情况,提出一种多尺度远距离点差值统计变换特征。通过Gabor滤波获得图像的多尺度特征信息,在像素图和滤波图上分别提取改进的远距离点差值统计变换特征,从而生成多尺度描述子,最后将多尺度统计变换特征和视觉词袋模型特征通过核协同表示融合后进行场景分类。该方法充分考虑了尺度信息和远距离点信息,解决了单一特征区分度低的问题。算法在两个标准数据集上进行对比实验,结果表明所提算法取得了较好的识别效果。In order to solve the problem of insufficient information provided by a single feature in complex scene recognition, a multi-scale census transform of difference of distant neighbors feature is proposed. Gabor filtering is used to obtain the multi-scale feature information of the image, and the improved census transform of difference of distant neighbors feature is extracted from the original image and the Gabor filtered image to generate a multi-scale descriptor. Finally, the multi-scale census transform features and bag-of-visual-word(BOVW) features are merged by kernel collaborative representation to classify scenes. This method considers the scale information and the long distance point information, and solves the problem of low discrimination of a single feature. The algorithm is verified on two standard datasets, and the experimental results show that the recognition rate of the proposed method is better.

关 键 词：场景分类 多尺度 统计特征 协同表示 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]