计算机应用与软件Issue(3)：195-198,4.DOI:10.3969/j.issn.1000-386x.2014.03.051
基于压缩感知理论的 RGB-D 图像分类方法
RGB-D IMAGES CLASSIFICATION BASED ON COMPRESSED SENSING THEORY
摘要
Abstract
3D images classification can effectively overcome the disadvantage of 2D colour images classification such as susceptible to the interferences of illumination changes,shadows,objects occlusion,environmental changes and other factors.In this paper,compressed sensingis used to study the classification of RGB-D images containing deep information gained by Kinect camera.First,the method uses downsampling and PCA to extract the features from RGB images and depth images separately.Secondly,the extracted features are fused.And then the sparse decomposition of the fused features is conducted by using compressed sensing and followed by classification.Finally,this method is used to make classification experiments on images in RGB-D dataset,including 6 kinds of vegetables,7 kinds of fruits,and the binder,the camera,counted 15 classes in total.The precision of RGB-D images classification using compressed sensing is compared with that of SVMclassifier;and the impact of depth information on image classification precision is compared and analysed as well.Experiments show that the classification precision of RGB-D images is higher by using compressed sensing than using SVM,and the classification precision of RGB-D images with depth information added is higher than that of RGB images.关键词
RGB-D 图像分类/压缩感知/特征提取/稀疏分解Key words
RGB-Dimages classification/Compressed sensing/Features extraction/Sparse decomposition分类
信息技术与安全科学引用本文复制引用
黄晓琳,薛月菊,涂淑琴,李鸿生,何金辉..基于压缩感知理论的 RGB-D 图像分类方法[J].计算机应用与软件,2014,(3):195-198,4.基金项目
国家科技支撑计划课题 （）
