Material Classification in theWild: Do Synthesized Training Data Generalise Better than Real-world Training Data?

Grigorios Kalliatakis, Grigorios Kalliatakis, Grigorios Kalliatakis, Grigorios Kalliatakis, Anca Sticlaru, Anca Sticlaru, Anca Sticlaru, Anca Sticlaru, George Stamatiadis, George Stamatiadis, George Stamatiadis, George Stamatiadis, Shoaib Ehsan, Shoaib Ehsan, Shoaib Ehsan, Shoaib Ehsan, Ales Leonardis, Ales Leonardis, Ales Leonardis, Ales Leonardis, Juergen Gall, Juergen Gall, Juergen Gall, Juergen Gall, Klaus D. McDonald-Maier, Klaus D. McDonald-Maier, Klaus D. McDonald-Maier, Klaus D. McDonald-Maier

Abstract

We question the dominant role of real-world training images in the field of material classification by investigating whether synthesized data can generalise more effectively than real-world data. Experimental results on three challenging real-world material databases show that the best performing pre-trained convolutional neural network (CNN) architectures can achieve up to 91.03% mean average precision when classifying materials in cross-dataset scenarios. We demonstrate that synthesized data achieve an improvement on mean average precision when used as training data and in conjunction with pre-trained CNN architectures, which spans from  5% to  19% across three widely used material databases of real-world images.

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Paper Citation


in Harvard Style

Kalliatakis G., Sticlaru A., Stamatiadis G., Ehsan S., Leonardis A., Gall J. and McDonald-Maier K. (2018). Material Classification in theWild: Do Synthesized Training Data Generalise Better than Real-world Training Data?.In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-290-5, pages 427-432. DOI: 10.5220/0006634804270432


in Bibtex Style

@conference{visapp18,
author={Grigorios Kalliatakis and Anca Sticlaru and George Stamatiadis and Shoaib Ehsan and Ales Leonardis and Juergen Gall and Klaus D. McDonald-Maier},
title={Material Classification in theWild: Do Synthesized Training Data Generalise Better than Real-world Training Data?},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2018},
pages={427-432},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006634804270432},
isbn={978-989-758-290-5},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - Material Classification in theWild: Do Synthesized Training Data Generalise Better than Real-world Training Data?
SN - 978-989-758-290-5
AU - Kalliatakis G.
AU - Sticlaru A.
AU - Stamatiadis G.
AU - Ehsan S.
AU - Leonardis A.
AU - Gall J.
AU - McDonald-Maier K.
PY - 2018
SP - 427
EP - 432
DO - 10.5220/0006634804270432