Joint global constraint and Fisher discrimination based multi-layer dictionary learning for image classification

A multi-layer dictionary learning algorithm that joints global constraints and Fisher discrimination(JGCFD-MDL)for image classification tasks was proposed.The algorithm reveals the manifold structure of the data by learning the global constraint dictionary and introduces the Fisher discriminative constraint dictionary to minimize the intra-class dispersion of samples and increase the inter-class dispersion.To further quantify the abstract features that characterize the data,a multi-layer dictionary learning framework is constructed to obtain high-level complex semantic structures and improve image classification performance.Finally,the algorithm is verified on the multi-label dataset of court costumes in the Ming Dynasty and Qing Dynasty,and better performance is obtained.Experiments show that compared with the local similarity algorithm,the average precision is improved by 3.34%.Compared with the single-layer dictionary learning algorithm,the one-error is improved by 1.00%,and the average precision is improved by 0.54%.Experiments also show that it has better performance on general datasets.