  Convolutional Networks have dominated the field of computer vision for the
last ten years, exhibiting extremely powerful feature extraction capabilities
and outstanding classification performance. The main strategy to prolong this
trend relies on further upscaling networks in size. However, costs increase
rapidly while performance improvements may be marginal. We hypothesise that
adding heterogeneous sources of information may be more cost-effective to a CNN
than building a bigger network. In this paper, an ensemble method is proposed
for accurate image classification, fusing automatically detected features
through Convolutional Neural Network architectures with a set of manually
defined statistical indicators. Through a combination of the predictions of a
CNN and a secondary classifier trained on statistical features, better
classification performance can be cheaply achieved. We test multiple learning
algorithms and CNN architectures on a diverse number of datasets to validate
our proposal, making public all our code and data via GitHub. According to our
results, the inclusion of additional indicators and an ensemble classification
approach helps to increase the performance in 8 of 9 datasets, with a
remarkable increase of more than 10% precision in two of them.
