Reference set based metric learning method for person re-identification against overfitting problem

Similarity distance measurement is an important method in data classification, data recognition and other tasks, and has a very wide range of applications in machine learning, computer vision and other fields. However, there are model overfitting problems in complex data classification identification tasks in existing metric learning model. And those problems will negatively infect the accuracy and stability of the metric models. We study on person re-identification (person re-ID) task to design a robust similarity distance metric learning model based on a novel approach of overcoming over-fitting problem. The proposed method sets up a reference set based on training sample. Using the reference set and test images to form similar sample pairs, we can optimize the distribution information and projection feature. Finally, by testing on benchmark dataset, VIPeR, the experimental results validate the effectiveness of the proposed method. It achieves the best identification rates.