Chi square kernel regularized linear discriminant analysis for person reidentification
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Abstract:
There are some problems in person reidentification, such as less training samples, no-linear relationship of samples, low recognition ratio. In order to solve these problems, the regularized linear discriminant analysis person reidentification algorithm (KRLDA) based on chi square kernel was proposed. Firstly, the algorithm mapped linear inseparable input data into a high dimensional linear separable feature space using kernel function to obtain scatter matrix that describes adjacent data relationship. Then, regularized linear discriminant analysis was applied to obtain low dimensional projection matrix for maintaining high dimensional separability characteristics to improve the recognition rate of the pedestrian re-recognition algorithm. Finally, experimental results on VIPeR, iLIDS, CAVIAR and 3DPeS datasets show that the proposed algorithm has a high recognition rate and the algorithm based on chi square kernel function has higher recognition rate than other kernel functions.
