  Here we propose and investigate the use of visibility graphs to model the
feature map of a neural network. The model, initially devised for studies on
complex networks, is employed here for the classification of texture images.
The work is motivated by an alternative viewpoint provided by these graphs over
the original data. The performance of the proposed method is verified in the
classification of four benchmark databases, namely, KTHTIPS-2b, FMD, UIUC, and
UMD and in a practical problem, which is the identification of plant species
using scanned images of their leaves. Our method was competitive with other
state-of-the-art approaches, confirming the potential of techniques used for
data analysis in different contexts to give more meaningful interpretation to
the use of neural networks in texture classification.
