Material Recognition : Bayesian Inference or SVMs ?

Material recognition is an important subtask in computer vision. In this paper, we aim for the identification of material categories from a single image captured under unknown illumination and view conditions. Therefore, we use several features which cover various aspects of material appearance and perform supervised classification using Support Vector Machines. We demonstrate the feasibility of our approach by testing on the challenging Flickr Material Database. Based on this dataset, we also carry out a comparison to a previously published work [Liu et al., ”Exploring Features in a Bayesian Framework for Material Recognition”, CVPR 2010] which uses Bayesian inference and reaches a recognition rate of 44.6% on this dataset and represents the current state-of the-art. With our SVM approach we obtain 53.1% and hence, significantly outperform this approach.

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