ML Anthology

Multi-Spectral SIFT for Scene Category Recognition

Matthew A. Brown, Sabine Süsstrunk
CVPR 2011 pp. 177-184
doi:10.1109/CVPR.2011.5995637 /cvpr/2011/brown2011cvpr-multi/

Abstract

We use a simple modification to a conventional SLR camera to capture images of several hundred scenes in colour (RGB) and near-infrared (NIR). We show that the addition of near-infrared information leads to significantly improved performance in a scene-recognition task, and that the improvements are greater still when an appropriate 4-dimensional colour representation is used. In particular we propose MSIFT â a multispectral SIFT descriptor that, when combined with a kernel based classifier, exceeds the performance of state-of-the-art scene recognition techniques (e.g., GIST) and their multispectral extensions. We extensively test our algorithms using a new dataset of several hundred RGB-NIR scene images, as well as benchmarking against Torralbaâs scene categorization dataset.

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Text

Brown and Süsstrunk. "Multi-Spectral SIFT for Scene Category Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2011. doi:10.1109/CVPR.2011.5995637

Markdown

[Brown and Süsstrunk. "Multi-Spectral SIFT for Scene Category Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2011.](https://mlanthology.org/cvpr/2011/brown2011cvpr-multi/) doi:10.1109/CVPR.2011.5995637

BibTeX

@inproceedings{brown2011cvpr-multi,
  title     = {{Multi-Spectral SIFT for Scene Category Recognition}},
  author    = {Brown, Matthew A. and Süsstrunk, Sabine},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2011},
  pages     = {177-184},
  doi       = {10.1109/CVPR.2011.5995637},
  url       = {https://mlanthology.org/cvpr/2011/brown2011cvpr-multi/}
}