Learning Multi-Scale Representations for Material Classification
This work addresses material classification in computer vision, offering a domain-specific improvement over existing methods.
The paper tackled material recognition by proposing a multi-scale coding procedure for feature learning, achieving results that outperform hand-crafted descriptors on the FMD and KTH-TIPS2 benchmarks.
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.