Multi-Label ReliefF and F-Statistic Feature Selections for Image Annotation

Deguang Kong, Chris H. Q. Ding, Heng Huang, Haifeng Zhao

CVPR 2012 pp. 2352-2359

doi:10.1109/CVPR.2012.6247947 /cvpr/2012/kong2012cvpr-multi/

Abstract

The classical ReliefF and F-statistic feature selections can not be directly applied into multi-label problems due to the ambiguity produced from a data point attributed to multiple classes simultaneously. In this paper, we present MReliefF and MF-statistic algorithms for multi-label feature selections. Discriminant features are selected to boost the multi-label classification accuracy. The proposed MReliefF and MF-statistic can be used in image categorization and annotation problems. Extensive experiments on image annotation tasks show the good performance of our approach. To our knowledge, this is the first work to generalize the ReliefF and F-statistic feature selection algorithms for multi-label image annotation tasks.

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Cite

Text

Kong et al. "Multi-Label ReliefF and F-Statistic Feature Selections for Image Annotation." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2012. doi:10.1109/CVPR.2012.6247947

Markdown

[Kong et al. "Multi-Label ReliefF and F-Statistic Feature Selections for Image Annotation." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2012.](https://mlanthology.org/cvpr/2012/kong2012cvpr-multi/) doi:10.1109/CVPR.2012.6247947

BibTeX

@inproceedings{kong2012cvpr-multi,
  title     = {{Multi-Label ReliefF and F-Statistic Feature Selections for Image Annotation}},
  author    = {Kong, Deguang and Ding, Chris H. Q. and Huang, Heng and Zhao, Haifeng},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2012},
  pages     = {2352-2359},
  doi       = {10.1109/CVPR.2012.6247947},
  url       = {https://mlanthology.org/cvpr/2012/kong2012cvpr-multi/}
}