Exploring Features in a Bayesian Framework for Material Recognition

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.

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Ce Liu, Lavanya Sharan, Edward Adelson, Ruth Rosenholtz
• Keywords: Augmented Latent Dirichlet, Computer Vision, CVPR 2010, Material Category, Visual Recognition Tasks