Multi-scale Collaborative Coupled Metric Learning Method Based on the Fusion of Class and Structure Information
ZOU Guofeng1, FU Guixia1, GAO Mingliang1, YIN Liju1, WANG Kejun2
1.College of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255049 2.College of Automation, Harbin Engineering University, Harbin 150001
Abstract：Aiming at the elements matching problem in different scale space sets, multi-scale collaborative coupled metric learning method based on the fusion of class and structure information is proposed. Firstly, the correlation matrix is constructed under the guidance of class information and structure information of sample distribution. The class information is significant for supervision and the structure information is the auxiliary supervision information. The linear and nonlinear optimization objective equations are constructed based on the correlation matrix. By solving the optimization objective equation, the samples are transformed from different scale space datasets into a unified public space for distance measurement. The experimental results of face recognition show that the nonlinear collaborative coupled metric is an effective measurement method and it is simple and convenient with a higher recognition rate.
邹国锋，傅桂霞，高明亮，尹丽菊，王科俊. 融合类别和结构信息的多尺度协同耦合度量学习方法*[J]. 模式识别与人工智能, 2017, 30(6): 499-508.
ZOU Guofeng, FU Guixia, GAO Mingliang, YIN Liju, WANG Kejun. Multi-scale Collaborative Coupled Metric Learning Method Based on the Fusion of Class and Structure Information. , 2017, 30(6): 499-508.
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2017, Vol. 30 
