Hierarchical Feature Hashing for Fast Dimensionality Reduction
Abstract
Curse of dimensionality is a practical and challenging problem in image categorization, especially in cases with a large number of classes. Multi-class classification encounters severe computational and storage problems when dealing with these large scale tasks. In this paper, we propose hierarchical feature hashing to effectively reduce dimensionality of parameter space without sacrificing classification accuracy, and at the same time exploit information in semantic taxonomy among categories. We provide detailed theoretical analysis on our proposed hashing method. Moreover, experimental results on object recognition and scene classification further demonstrate the effectiveness of hierarchical feature hashing.
Cite
Text
Zhao and Xing. "Hierarchical Feature Hashing for Fast Dimensionality Reduction." Conference on Computer Vision and Pattern Recognition, 2014. doi:10.1109/CVPR.2014.263Markdown
[Zhao and Xing. "Hierarchical Feature Hashing for Fast Dimensionality Reduction." Conference on Computer Vision and Pattern Recognition, 2014.](https://mlanthology.org/cvpr/2014/zhao2014cvpr-hierarchical/) doi:10.1109/CVPR.2014.263BibTeX
@inproceedings{zhao2014cvpr-hierarchical,
title = {{Hierarchical Feature Hashing for Fast Dimensionality Reduction}},
author = {Zhao, Bin and Xing, Eric P.},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2014},
doi = {10.1109/CVPR.2014.263},
url = {https://mlanthology.org/cvpr/2014/zhao2014cvpr-hierarchical/}
}