Improving CNN-Based Texture Classification by Color Balancing
AbstractTexture 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
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Bianco, S.; Cusano, C.; Napoletano, P.; Schettini, R. Improving CNN-Based Texture Classification by Color Balancing. J. Imaging 2017, 3, 33.
Bianco S, Cusano C, Napoletano P, Schettini R. Improving CNN-Based Texture Classification by Color Balancing. Journal of Imaging. 2017; 3(3):33.Chicago/Turabian Style
Bianco, Simone; Cusano, Claudio; Napoletano, Paolo; Schettini, Raimondo. 2017. "Improving CNN-Based Texture Classification by Color Balancing." J. Imaging 3, no. 3: 33.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.