ML Anthology

Error Factor Analysis for Wild Scene Image-Labelling

Peng Wang, Alan L. Yuille
WACV 2015 pp. 781-788
doi:10.1109/WACV.2015.109 /wacv/2015/wang2015wacv-error/

Abstract

PASCAL VOC Segmentation Challenge [10] is currently considered as one of the datasets that reflect the image segmentation difficulties for real world scenarios [29]. However, current evaluation is simply based on a single Inter-section Over Union (IOU) score. In this paper, we try to discover the error factors under the IOU, which makes the results more informative to understand rather than a black box. Specifically, we decompose the error into three error types in terms of object characteristics, i.e. general, appearance and shape. Each error type is composed of respective factors, e.g. size and aspect ratio for general, appearance distinctiveness for appearance, etc. Finally, for each factor and error type, we perform analysis over its impact on and correlation with the final IOU through robust regression. Our experiments show that these error factors have significant relationship with the given IOU accuracy, and the analysis provides practical guidance on further improvement of the given algorithm.

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Cite

Text

Wang and Yuille. "Error Factor Analysis for Wild Scene Image-Labelling." IEEE/CVF Winter Conference on Applications of Computer Vision, 2015. doi:10.1109/WACV.2015.109

Markdown

[Wang and Yuille. "Error Factor Analysis for Wild Scene Image-Labelling." IEEE/CVF Winter Conference on Applications of Computer Vision, 2015.](https://mlanthology.org/wacv/2015/wang2015wacv-error/) doi:10.1109/WACV.2015.109

BibTeX

@inproceedings{wang2015wacv-error,
  title     = {{Error Factor Analysis for Wild Scene Image-Labelling}},
  author    = {Wang, Peng and Yuille, Alan L.},
  booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
  year      = {2015},
  pages     = {781-788},
  doi       = {10.1109/WACV.2015.109},
  url       = {https://mlanthology.org/wacv/2015/wang2015wacv-error/}
}