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arXiv · 1711.03874

Material Classification in the Wild: Do Synthesized Training Data Generalise Better than Real-World Training Data?

Grigorios Kalliatakis·Anca Sticlaru·George Stamatiadis·Shoaib Ehsan·Ales Leonardis·Juergen Gall·Klaus D. McDonald-Maier
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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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Grigorios Kalliatakis, Anca Sticlaru, George Stamatiadis, Shoaib Ehsan, Ales Leonardis, Juergen Gall, Klaus D. McDonald-Maier. 2017-11-09. Material Classification in the Wild: Do Synthesized Training Data Generalise Better than Real-World Training Data?. https://arxiv.org/abs/1711.03874

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