Research On Image Classification Method Based On Multi-view Collaborative Representation

As a research hotspot in the field of computer vision and pattern recognition,image classification has important application value.At present,researchers have proposed many classification algorithms,but for fine-grained images with large intra-class differences and small inter-class differences,or images obtained under different shooting ratios,shooting angles and different seasons,such as remote sensing images,these classification methods may make classification errors.In view of the images acquired under these specific conditions,this paper conducts an in-depth study on the representation and classification methods of fine-grained images and remote sensing images from the perspectives of multi-scale,multi-data and multicategory,based on sparse coding and machine learning theory.The main work of the paper is as follows:1.A matching collaborative representation classification algorithm based on weight space pyramid is proposed.Firstly,convolutional neural network is applied to extract image features and spatial pyramid matching algorithm is introduced to obtain perspective features at different scales.Then,different weights are assigned to each feature layer to obtain image features with spatial information,which can be used as input of the weighted spatial pyramid matching collaborative representation classification algorithm.Experimental results on four remote sensing databases show that the weighted space pyramid matching cooperative representation algorithm improves the performance of image classification.2.A classification algorithm based on kernel space superposition is proposed.The recognition problem is expressed by representing the test image as the superposition of the centroid and the difference of common views within the class.In order to further obtain the nonlinear information between data,the linear superposition classification algorithm is extended to the kernel space.Experimental results on five comprehensive databases show that the classification algorithm based on kernel space get higher classification accuracy.3.A probabilistic collaborative representation classification algorithm based on kernel space is proposed.The traditional collaborative representation classification algorithm is improved from the perspective of global representation and specific class representation,and the probabilistic collaborative representation classification algorithm is extended to the kernel space to further obtain the nonlinear structure between visual features.Experimental results on several benchmark data sets such as handwritten digital recognition,standard image recognition and face recognition show that the core-space probabilistic cooperative representation classification algorithm has good classification performance.