Learning Multi-scale Representations for Material Classification

Conference paper

DOI: 10.1007/978-3-319-11752-2_65

Volume 8753 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Li W. (2014) Learning Multi-scale Representations for Material Classification. In: Jiang X., Hornegger J., Koch R. (eds) Pattern Recognition. GCPR 2014. Lecture Notes in Computer Science, vol 8753. Springer, Cham

Abstract

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.

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Max Planck Institute for InformaticsSaarbrückenGermany