| 摘要 |
A method of operating a computer system to perform material recognition based on multiple features extracted from an image is described. A combination of low-level features extracted directly from the image and multiple novel mid-level features extracted from transformed versions of the image are selected and used to assign a material category to a single image. The novel mid-level features include non-reflectance based features such as the micro-texture features micro jet and micro-SIFT and the shape feature curvature, and reflectance-based features including edge slice and edge ribbon. An augmented Latent Dirichlet Allocation (LDA) model is provided as an exemplary Bayesian framework for selecting a subset of features useful for material recognition of objects in an image.
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