Learning Non-Linear Calibration for Score Fusion with Applications to Image and Video Classification
Abstract
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 the other base classifiers, in such a way that calibrated scores can be simply added to yield accurate fusion results. Our approach provides a unified approach to jointly solve score normalization and fusion classifier learning. The learning problem is solved within a max-margin framework to globally optimize performance metric on the training set. Experiments demonstrate the strength and robustness of the proposed method.
Cite
Text
Ma et al. "Learning Non-Linear Calibration for Score Fusion with Applications to Image and Video Classification." IEEE/CVF International Conference on Computer Vision Workshops, 2013. doi:10.1109/ICCVW.2013.50Markdown
[Ma et al. "Learning Non-Linear Calibration for Score Fusion with Applications to Image and Video Classification." IEEE/CVF International Conference on Computer Vision Workshops, 2013.](https://mlanthology.org/iccvw/2013/ma2013iccvw-learning/) doi:10.1109/ICCVW.2013.50BibTeX
@inproceedings{ma2013iccvw-learning,
title = {{Learning Non-Linear Calibration for Score Fusion with Applications to Image and Video Classification}},
author = {Ma, Tianyang and Oh, Sangmin and Perera, Amitha and Latecki, Longin Jan},
booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
year = {2013},
pages = {323-330},
doi = {10.1109/ICCVW.2013.50},
url = {https://mlanthology.org/iccvw/2013/ma2013iccvw-learning/}
}