Local manifold sparse model for image classification

How to discriminate the types of images is very important for image understanding. A large number of classifiers have been designed for the automatic classification of image. In recent years, sparse representation has widely used in the field of image classification. However, most of the sparse classification methods are based on the sparse reconstruction which ignores the intrinsic structure of data. Therefore, in this paper, we proposed a local manifold sparse classifier (LMSC) on the basis of the sparse manifold assumption and the local structure of data. The proposed method uses the sparse manifold assumption and the local neighbors of data to construct a sparse representation model. Then, we can obtain the sparse coefficients with the proposed sparse model. Finally, we can calculate the probability of the unknown image in each class and assign the class label of the maximization probability to this image. LMSC can reveal the intrinsic structure of data and improve the accuracy of image classification. Experiments on two handwritten digit image data sets (MNIST and Semeion) and a hyperspectral remote sensing image data set (Pavia university) show that the proposed method can achieve better representation and classification accuracy compared to some state-of-the-art classification methods. (C) 2019 Elsevier B.V. All rights reserved.