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.
Bianco, S., Cusano, C., Napoletano, P., & Schettini, R. (2017). Improving CNN-based texture classification by color balancing. JOURNAL OF IMAGING, 3(3), 1-15 [10.3390/jimaging3030033].
|Citazione:||Bianco, S., Cusano, C., Napoletano, P., & Schettini, R. (2017). Improving CNN-based texture classification by color balancing. JOURNAL OF IMAGING, 3(3), 1-15 [10.3390/jimaging3030033].|
|Tipo:||Articolo in rivista - Articolo scientifico|
|Carattere della pubblicazione:||Scientifica|
|Presenza di un coautore afferente ad Istituzioni straniere:||No|
|Titolo:||Improving CNN-based texture classification by color balancing|
|Autori:||Bianco, S; Cusano, C; Napoletano, P; Schettini, R|
BIANCO, SIMONE (Corresponding)
CUSANO, CLAUDIO (Corresponding)
NAPOLETANO, PAOLO (Corresponding)
SCHETTINI, RAIMONDO (Corresponding)
|Data di pubblicazione:||2017|
|Rivista:||JOURNAL OF IMAGING|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.3390/jimaging3030033|
|Appare nelle tipologie:||01 - Articolo su rivista|