Determining the material category of a surface from an image is a demanding task in perception that is drawing increasing attention. Following the recent remarkable results achieved for image classification and object detection utilising Convolutional Neural Networks (CNNs), we empirically study material classification of everyday objects employing these techniques. More specifically, we conduct a rigorous evaluation of how state-of-the art CNN architectures compare on a common ground over widely used material databases. Experimental results on three challenging material databases show that the best performing CNN architectures can achieve up to 94.99\% mean average precision when classifying materials.
@article{arxiv.1703.04101,
title = {Evaluating Deep Convolutional Neural Networks for Material Classification},
author = {Grigorios Kalliatakis and Georgios Stamatiadis and Shoaib Ehsan and Ales Leonardis and Juergen Gall and Anca Sticlaru and Klaus D. McDonald-Maier},
journal= {arXiv preprint arXiv:1703.04101},
year = {2017}
}
Comments
In Proceedings of the 12th International Conference on Computer Vision Theory and Applications (VISAPP 2017), 7 pages