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Exploring Features in a Bayesian Framework for Material Recognition

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

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.

Information

title:
Exploring Features in a Bayesian Framework for Material Recognition
author:
Ce Liu,
Lavanya Sharan,
Edward H. Adelson,
and Ruth Rosenholtz
citation:
EEE Conference on Computer Vision and Pattern Recognition (CVPR) pp239 - 246
shortcite:
CVPR 2010
year:
2010
created:
2010-01-01
keyword:
adelson,
rosenholtz
summary:
maltrecogcvpr10
pdf:
http://persci.mit.edu/pub_pdfs/maltRecogCVPR10.pdf
pageid:
maltRecogCVPR10
type:
publication
 
publications/maltrecogcvpr10.1299785747.txt.gz · Last modified: 2011/03/10 14:35 by elmer