Kernel Sparse Representation Classifier with Center Enhanced SPM for Vehicle Classification
In this paper, we proposes a visual-based vehicle classification system, in which it involves visual feature representation and classification step. In the feature representation step, we present a center enhanced spatial pyramid matching (CE-SPM) to extract the feature from images. In this work, we defined additional region in the center of each images to calculate the histograms of visual words and then pool them together with some weights to construct the feature representation vector of an image. In the classification step, kernel sparse representation classifier is used to address the problem of visual-based vehicle classification. The kernel function maps the features from original space into higher space dimension. The modified active-set algorithm for l1 non-negative least square problem is adopted to solve the optimization problem. The experimental results show the improvement of proposed method over the original SPM. The proposed method can achieve the performance of 93.7% using particular vehicle image dataset.