The invention relates to an RGB-D image classification method and
system. The method comprises the steps of: S1, utilizing a
convolution neural network (CNN) to process a source
RGB image and a Depth image respectively, and extracting low level characteristics; S2, utilizing a
recursion neural network (RNN) to carry out feedback learning on the image low level characteristics, and extracting image
middle level characteristics; S3, adopting a block interior constraint
dictionary learning method, carrying out characteristic set sparse expression on the image
middle level characteristics, and obtaining high level characteristics of the RGB-D images; and S4, inputting the high level characteristics of the RGB-D images into a
linear SVM to complete the classified identification of the RGB-D images. According to the invention, automatic characteristic extraction of the images is realized, learning RGB-D image characteristic expressions can effectively distinguish classification of
noise data from high similarity images, and the classification precision of the RGB-D images is improved; in addition, the
linear SVM is utilized, and the image classification speed is improved.