ML Anthology

Object Recognition with Features Inspired by Visual Cortex

Thomas Serre, Lior Wolf, Tomaso A. Poggio
CVPR 2005 pp. 994-1000
doi:10.1109/CVPR.2005.254 /cvpr/2005/serre2005cvpr-object/

Abstract

We introduce a novel set of features for robust object recognition. Each element of this set is a complex feature obtained by combining position- and scale-tolerant edge-detectors over neighboring positions and multiple orientations. Our system's architecture is motivated by a quantitative model of visual cortex. We show that our approach exhibits excellent recognition performance and outperforms several state-of-the-art systems on a variety of image datasets including many different object categories. We also demonstrate that our system is able to learn from very few examples. The performance of the approach constitutes a suggestive plausibility proof for a class of feedforward models of object recognition in cortex.

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Text

Serre et al. "Object Recognition with Features Inspired by Visual Cortex." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005. doi:10.1109/CVPR.2005.254

Markdown

[Serre et al. "Object Recognition with Features Inspired by Visual Cortex." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005.](https://mlanthology.org/cvpr/2005/serre2005cvpr-object/) doi:10.1109/CVPR.2005.254

BibTeX

@inproceedings{serre2005cvpr-object,
  title     = {{Object Recognition with Features Inspired by Visual Cortex}},
  author    = {Serre, Thomas and Wolf, Lior and Poggio, Tomaso A.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2005},
  pages     = {994-1000},
  doi       = {10.1109/CVPR.2005.254},
  url       = {https://mlanthology.org/cvpr/2005/serre2005cvpr-object/}
}