Learning Euclidean-to-Riemannian metric for point-to-set classification

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2014, Pages 1677-1684

  Huang, Zhiwu ^a,b   Wang, Ruiping ^a   Shan, Shiguang ^a   Chen, Xilin ^a  

^a INSTITUTE OF COMPUTING TECHNOLOGY   (China)

^b UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

Author keywords

Euclidean to Riemannian metric learning; point to set classification

Indexed keywords

HILBERT SPACES; MATHEMATICAL TRANSFORMATIONS; PATTERN RECOGNITION; VECTOR SPACES;

EUCLIDEAN DISTANCE; EUCLIDEAN SPACES; HIGH-DIMENSIONAL; MULTIPLE TRANSFORMATION; RIEMANNIAN GEOMETRY; RIEMANNIAN MANIFOLD; RIEMANNIAN METRICS; STATE-OF-THE-ART METHODS;

GEOMETRY;

EID: 84911393747     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2014.217     Document Type: Conference Paper

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