Metric learning by simultaneously learning linear transformation matrix and weight matrix for person re‐identification

Abstract

Mahalanobis metric learning is one of the most popular methods for person re‐identification. Most existing metric learning methods regularly formulate the person re‐identification as an unconstrained optimisation problem and the constraints on the Mahalanobis matrix are seldom imposed. In addition, weights are often used to model the relationships between different variables but they often suffer from boundedness caused by their hand‐designed feature. Taking the above two disadvantages into consideration, the authors propose a new metric learning method for person re‐identification, which formulates the metric learning problem as a constrained optimisation problem by imposing a constraint on the linear transformation matrix. Furthermore, they treat the weights as unknown variables and introduce a weight learning method instead of designing weight intuitively. Finally, they evaluate the proposed method on two challenging person re‐identification databases and show that it performs favourably against the state‐of‐the‐art approaches.

Keywords

• constrained optimisation problem
• linear transformation matrix
• weight learning method
• Mahalanobis metric learning
• unconstrained optimisation problem
• Mahalanobis matrix