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Learning Non-linear Calibration for Score Fusion with Applications to Image and Video Classification
2013
2013 IEEE International Conference on Computer Vision Workshops
Image and video classification is a challenging task, particularly for complex real-world data. Recent work indicates that using multiple features can improve classification significantly, and that score fusion is effective. In this work, we propose a robust score fusion approach which learns non-linear score calibrations for multiple base classifier scores. Through calibration, original base classifiers scores are adjusted to reflect their true intrinsic accuracy and confidence, relative to
doi:10.1109/iccvw.2013.50
dblp:conf/iccvw/MaOPL13
fatcat:p3m3r7k2tvh6rn33lqf3ecu6dq