Exploring features in a Bayesian framework for material recognition
Author(s)Liu, Ce; Sharan, Lavanya; Adelson, Edward H.; Rosenholtz, Ruth
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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.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Proceedings of the 2010 IEEE Conference on Computer Vision and Pattern Recognition, (CVPR)
Institute of Electrical and Electronics Engineers
Liu, Ce et al. “Exploring Features in a Bayesian Framework for Material Recognition.” IEEE, 2010 Computer Vision and Pattern Recognition (CVPR), IEEE Conference on, p. 239–246. © Copyright 2010 IEEE.
Final published version
INSPEC Accession Number: 11500638