Research On Image Classification Based On Structured Dictionary Learning

Image classification is a very active research field in computer vision,multimedia,pattern recognition and machine learning.Image classification has been widely used in many fields,including face recognition,intelligent video analysis,pedestrian tracking,etc.It can be said that the image classification has been applied to all aspects of people's daily life,the computer automatic classification technology to some extent,reduce the burden on people,change the way of human life.In recent years,sparse coding models have been widely used in the field of image classification and video processing.In this model,the image is expressed as a linear combination of a set of atoms of the learned dictionaries.Research shows that in most image processing applications,such as face recognition,object recognition,image classification,compared to non supervised dictionary learning method,the supervised dictionary learning method based on the hierarchical relations of the categories achieve better experimental performance.In this paper,we mainly study the structured dictionary learning model and its application in image classification.The main research contents are as follows:1.Summarize the research progress of the non supervised dictionary learning and supervised learning in the related fields.2.Propose a new graph-guided supervised dictionary learning algorithm,which is based on the hierarchical category structure.The experimental results are analyzed and summarized.3.Propose a new dictionary learning method based on hierarchical relation and variable relationship.The effectiveness of the proposed method in the field of image classification is demonstrated by experiments.