  The recent progress in sparse coding and deep learning has made unsupervised
feature learning methods a strong competitor to hand-crafted descriptors. In
computer vision, success stories of learned features have been predominantly
reported for object recognition tasks. In this paper, we investigate if and how
feature learning can be used for material recognition. We propose two
strategies to incorporate scale information into the learning procedure
resulting in a novel multi-scale coding procedure. Our results show that our
learned features for material recognition outperform hand-crafted descriptors
on the FMD and the KTH-TIPS2 material classification benchmarks.

Computer Vision and Pattern Recognition;Machine Learning;Neural and Evolutionary Computing