ML Anthology

Scenes vs. Objects: A Comparative Study of Two Approaches to Context Based Recognition

Andrew Rabinovich, Serge J. Belongie
CVPRW 2009 pp. 92-99
doi:10.1109/CVPRW.2009.5204220 /cvprw/2009/rabinovich2009cvprw-scenes/

Abstract

Contextual models play a very important role in the task of object recognition. Over the years, two kinds of contextual models have emerged: models with contextual inference based on the statistical summary of the scene (we will refer to these as scene based context models, or SBC), and models representing the context in terms of relationships among objects in the image (object based context, or OBC). In designing object recognition systems, it is necessary to understand the theoretical and practical properties of such approaches. This work provides an analysis of these models and evaluates two of their representatives using the LabelMe dataset. We demonstrate a considerable margin of improvement using the OBC style approach.

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Text

Rabinovich and Belongie. "Scenes vs. Objects: A Comparative Study of Two Approaches to Context Based Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2009. doi:10.1109/CVPRW.2009.5204220

Markdown

[Rabinovich and Belongie. "Scenes vs. Objects: A Comparative Study of Two Approaches to Context Based Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2009.](https://mlanthology.org/cvprw/2009/rabinovich2009cvprw-scenes/) doi:10.1109/CVPRW.2009.5204220

BibTeX

@inproceedings{rabinovich2009cvprw-scenes,
  title     = {{Scenes vs. Objects: A Comparative Study of Two Approaches to Context Based Recognition}},
  author    = {Rabinovich, Andrew and Belongie, Serge J.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2009},
  pages     = {92-99},
  doi       = {10.1109/CVPRW.2009.5204220},
  url       = {https://mlanthology.org/cvprw/2009/rabinovich2009cvprw-scenes/}
}