Open AccessPerson re‐identification by graph‐based metric fusion
Author(s) -
Xie Yi,
Levine Martin D.,
Yu Huimin
Publication year - 2016
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2016.2109
Subject(s) - metric (unit) , identification (biology) , artificial intelligence , computer science , graph , machine learning , pattern recognition (psychology) , computer vision , theoretical computer science , operations management , botany , economics , biology
Person re‐identification is defined as re‐identifying individuals across different camera views. This is a very challenging problem since the appearance of a person can vary significantly due to cross‐camera changes in viewpoint, pose and illumination. To model the transition between camera views, distance metric learning has been widely used in person re‐identification and shown to be effective. However, using one specific metric often suffers from over‐fitting and may not be sufficient enough to cope with the cross‐camera variations of all different individuals. In this Letter, a powerful metric fusion method is proposed to combine multiple given distance metrics. Specifically, we represent given metrics as different graphs and then formulate the fusion problem as a graph‐based learning framework. In this way, our framework can efficiently integrate the complementary information provided by different input metrics.