An Image Classification Method Based on Locality-Constrained Sparse Coding with Ranking Locality Adaptor

Image classification is an important research direction in the field of computer vision analysis. The key to classification depends largely on the feature representation of the image. In order to be able to classify images better, this paper presents a locality-constrained sparse coding for image classification by introducing ranking locality adaptor as distance regularization. The proposed ranking locality adaptor has previously been used in Neural Gas (NG) method for vector quantization, which originally remedies the K-means by using this soft-competitive learning scheme. In the new proposed method, a closed-form solution can be derived at sparse coding step. In addition, dictionary updates are generally determined by the error term of the objective function. Some classical algorithms have used this method to update the dictionary. This paper uses the ORL database and COIL20 database to compare the proposed algorithm with the existing algorithm Locality-constrained Linear Coding, and Metaface Learning algorithm. The experimental results show that the proposed algorithm has an accuracy of more than 95% in image classification and has stronger performance than the current excellent algorithms. In addition, the recognition rate of the algorithm does not change greatly with the change of the data feature dimension. It can be seen that this paper provides a valuable solution for the classification of computer vision images. © 2019, Chinese Institute of Electronics. All right reserved.