Sparse representation for image recognition based on semi-genetic algorithm in feature space - Linrui SHI; Yijing HUANG; Jinwu FU; Xinyue GUO; Zizhu FAN (智能科学与技术学报, 2021)
The typical sparse representation for classification (SRC) is usually based on L1minimization problem.Conceptually, SRC is essentially an L0norm minimization problem solved in the original input space, which cannot capture well the nonlinear information within the data.In order to address this problem, a nonlinear mapping to map the original input data into a new high dimensional feature space was applied, and a new representation approach based on L0norm was proposed.The representing dictionary used to represent the test sample contains two parts in the proposed approach.The first part is fixed to the neighbors of the test sample.The training samples of the second part is chosen by the variation of genetic algorithm (GA), i.e., the semi GA (SGA) algorithm, which exploits the representation error to determine the second part of the representing dictionary.In the approach, if the training samples combining the determined neighbors of the test sample yield the least representation error, these training samples are determined as the second part of the representing dictionary by SGA.Experiments on several popular face databases and one handwritten digit data set demonstrate that the proposed approach can achieve better classification performance.