Abstract
In the sparse representation-based classification (SRC), the object recognition procedure depends on local sparsity identification from sparse coding coefficients, where many existing SRC methods have focused on the local sparsity and the samples correlation to improve the classifier performance. However, the coefficients often do not accurately represent the local sparsity due to several factors that affect the data acquisition process such as noise, blurring, and downsampling. Therefore, this paper presents an effective method that exploits nonlocal sparsity by estimating the sparse code changes, which can be done by adding a nonlocal constraint term to the local constraint one. In addition, for generality, the sparse coding and regularization parameters are adaptively estimated. A comparative study demonstrated that the proposed method has better accuracy rates compared to the existing state-of-the-art methods.