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Learning Multi-scale Representations for Material Classification

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons85396

Li,  Wenbin
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44451

Fritz,  Mario
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

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Volltexte (frei zugänglich)

arXiv:1408.2938.pdf
(Preprint), 3MB

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Zitation

Li, W., & Fritz, M. (2014). Learning Multi-scale Representations for Material Classification. Retrieved from http://arxiv.org/abs/1408.2938.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0024-3527-8
Zusammenfassung
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.