ML Anthology

Food Recognition Using Statistics of Pairwise Local Features

Shulin Yang, Mei Chen, Dean Pomerleau, Rahul Sukthankar
CVPR 2010 pp. 2249-2256
doi:10.1109/CVPR.2010.5539907 /cvpr/2010/yang2010cvpr-food/

Abstract

Food recognition is difficult because food items are de-formable objects that exhibit significant variations in appearance. We believe the key to recognizing food is to exploit the spatial relationships between different ingredients (such as meat and bread in a sandwich). We propose a new representation for food items that calculates pairwise statistics between local features computed over a soft pixel-level segmentation of the image into eight ingredient types. We accumulate these statistics in a multi-dimensional histogram, which is then used as a feature vector for a discriminative classifier. Our experiments show that the proposed representation is significantly more accurate at identifying food than existing methods.

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Cite

Text

Yang et al. "Food Recognition Using Statistics of Pairwise Local Features." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2010. doi:10.1109/CVPR.2010.5539907

Markdown

[Yang et al. "Food Recognition Using Statistics of Pairwise Local Features." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2010.](https://mlanthology.org/cvpr/2010/yang2010cvpr-food/) doi:10.1109/CVPR.2010.5539907

BibTeX

@inproceedings{yang2010cvpr-food,
  title     = {{Food Recognition Using Statistics of Pairwise Local Features}},
  author    = {Yang, Shulin and Chen, Mei and Pomerleau, Dean and Sukthankar, Rahul},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2010},
  pages     = {2249-2256},
  doi       = {10.1109/CVPR.2010.5539907},
  url       = {https://mlanthology.org/cvpr/2010/yang2010cvpr-food/}
}