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J. Imaging 2017, 3(3), 33; doi:10.3390/jimaging3030033

Improving CNN-Based Texture Classification by Color Balancing

1
Department of Computer Science, Systems and Communications, University of Milano-Bicocca, 20126 Milan, Italy
2
Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy
*
Author to whom correspondence should be addressed.
Received: 29 June 2017 / Revised: 17 July 2017 / Accepted: 21 July 2017 / Published: 27 July 2017
(This article belongs to the Special Issue Color Image Processing)
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Abstract

Texture classification has a long history in computer vision. In the last decade, the strong affirmation of deep learning techniques in general, and of convolutional neural networks (CNN) in particular, has allowed for a drastic improvement in the accuracy of texture recognition systems. However, their performance may be dampened by the fact that texture images are often characterized by color distributions that are unusual with respect to those seen by the networks during their training. In this paper we will show how suitable color balancing models allow for a significant improvement in the accuracy in recognizing textures for many CNN architectures. The feasibility of our approach is demonstrated by the experimental results obtained on the RawFooT dataset, which includes texture images acquired under several different lighting conditions. View Full-Text
Keywords: convolutional neural networks; color balancing; deep learning; texture classification; color constancy; color characterization convolutional neural networks; color balancing; deep learning; texture classification; color constancy; color characterization
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Bianco, S.; Cusano, C.; Napoletano, P.; Schettini, R. Improving CNN-Based Texture Classification by Color Balancing. J. Imaging 2017, 3, 33.

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