Robust Image Recognition Algorithm of Maximum Likelihood Estimation Sparse Representation Based on Class-related Neighbors Subspace
基于类别相关近邻子空间的最大似然稀疏表示鲁棒图像识别算法

In order to construct a fast and robust image recognition algorithm, an image recognition algorithm of maximum likelihood estimation sparse representation based on class-related neighbors subspace is proposed in this paper. Considering the different distribution characteristics of each test sample and the class-representative principle of training samples' selection, instead of constructing the dictionary of sparse representation by all training samples, suitable subspace is selected and local neighbors of adaptive number that is selected from each class are used to construct the new dictionary based on distance proximity criterion. The training samples are reduced and the original subspace structure of sparse representation is kept at the same time. Then based on the recognition method of maximum likelihood sparse representation, the fidelity of sparse representation is represented by the maximum likelihood function of residuals and the recognition problem is converted to a weighted sparse optimization problem. Experiments results on public available face and handwritten digital databases verify the rationality, recognition speed, and recognition accuracy of the proposed algorithm. The algorithm is robust, especially it can work for in disturbed and occluded images.