ML Anthology

Learning a Discriminative Classifier Using Shape Context Distances

Hao Zhang, Jitendra Malik
CVPR 2003 pp. 242-247
doi:10.1109/CVPR.2003.1211360 /cvpr/2003/zhang2003cvpr-learning/

Abstract

For the purpose of object recognition, we learn one discriminative classifier based on one prototype, using shape context distances as the feature vector. From multiple prototypes, the outputs of the classifiers are combined using the method called "error correcting output codes". The overall classifier is tested on a benchmark dataset and is shown to outperform existing methods with far fewer prototypes.

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Cite

Text

Zhang and Malik. "Learning a Discriminative Classifier Using Shape Context Distances." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2003. doi:10.1109/CVPR.2003.1211360

Markdown

[Zhang and Malik. "Learning a Discriminative Classifier Using Shape Context Distances." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2003.](https://mlanthology.org/cvpr/2003/zhang2003cvpr-learning/) doi:10.1109/CVPR.2003.1211360

BibTeX

@inproceedings{zhang2003cvpr-learning,
  title     = {{Learning a Discriminative Classifier Using Shape Context Distances}},
  author    = {Zhang, Hao and Malik, Jitendra},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2003},
  pages     = {242-247},
  doi       = {10.1109/CVPR.2003.1211360},
  url       = {https://mlanthology.org/cvpr/2003/zhang2003cvpr-learning/}
}