ML Anthology

Gist: A Mobile Robotics Application of Context-Based Vision in Outdoor Environment

Christian Siagian, Laurent Itti
CVPR 2005 pp. 88
doi:10.1109/CVPR.2005.465 /cvpr/2005/siagian2005cvpr-gist/

Abstract

We present context-based scene recognition for mobile robotics applications. Our classifier is able to differentiate outdoor scenes without temporal filtering relatively well from a variety of locations at a college campus using a set of features that together capture the "gist" of the scene. We compare the classification accuracy of a set of scenes from 1551 frames filmed outdoors along a path and dividing them to four and twelve different legs while obtaining a classifi- cation rate of 67.96 percent and 48.61 percent, respectively. We also tested the scalability of the features by comparing the classification results from the previous scenes with four legs with a longer path with eleven legs while obtaining a classification rate of 55.08 percent. In the end, some ideas are put forth to improve the theoretical strength of the gist features.

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Cite

Text

Siagian and Itti. "Gist: A Mobile Robotics Application of Context-Based Vision in Outdoor Environment." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005. doi:10.1109/CVPR.2005.465

Markdown

[Siagian and Itti. "Gist: A Mobile Robotics Application of Context-Based Vision in Outdoor Environment." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005.](https://mlanthology.org/cvpr/2005/siagian2005cvpr-gist/) doi:10.1109/CVPR.2005.465

BibTeX

@inproceedings{siagian2005cvpr-gist,
  title     = {{Gist: A Mobile Robotics Application of Context-Based Vision in Outdoor Environment}},
  author    = {Siagian, Christian and Itti, Laurent},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2005},
  pages     = {88},
  doi       = {10.1109/CVPR.2005.465},
  url       = {https://mlanthology.org/cvpr/2005/siagian2005cvpr-gist/}
}