Exploring Features in a Bayesian Framework for Material Recognition

Ce Liu, Lavanya Sharan, Edward H. Adelson, Ruth Rosenholtz

CVPR 2010 pp. 239-246

doi:10.1109/CVPR.2010.5540207 /cvpr/2010/liu2010cvpr-exploring/

Abstract

We are interested in identifying the material category, e.g. glass, metal, fabric, plastic or wood, from a single image of a surface. Unlike other visual recognition tasks in computer vision, it is difficult to find good, reliable features that can tell material categories apart. Our strategy is to use a rich set of low and mid-level features that capture various aspects of material appearance. We propose an augmented Latent Dirichlet Allocation (aLDA) model to combine these features under a Bayesian generative framework and learn an optimal combination of features. Experimental results show that our system performs material recognition reasonably well on a challenging material database, outperforming state-of-the-art material/texture recognition systems.

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Cite

Text

Liu et al. "Exploring Features in a Bayesian Framework for Material Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2010. doi:10.1109/CVPR.2010.5540207

Markdown

[Liu et al. "Exploring Features in a Bayesian Framework for Material Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2010.](https://mlanthology.org/cvpr/2010/liu2010cvpr-exploring/) doi:10.1109/CVPR.2010.5540207

BibTeX

@inproceedings{liu2010cvpr-exploring,
  title     = {{Exploring Features in a Bayesian Framework for Material Recognition}},
  author    = {Liu, Ce and Sharan, Lavanya and Adelson, Edward H. and Rosenholtz, Ruth},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2010},
  pages     = {239-246},
  doi       = {10.1109/CVPR.2010.5540207},
  url       = {https://mlanthology.org/cvpr/2010/liu2010cvpr-exploring/}
}