  Features play a crucial role in computer vision. Initially designed to detect
salient elements by means of handcrafted algorithms, features are now often
learned by different layers in Convolutional Neural Networks (CNNs). This paper
develops a generic computer vision system based on features extracted from
trained CNNs. Multiple learned features are combined into a single structure to
work on different image classification tasks. The proposed system was
experimentally derived by testing several approaches for extracting features
from the inner layers of CNNs and using them as inputs to SVMs that are then
combined by sum rule. Dimensionality reduction techniques are used to reduce
the high dimensionality of inner layers. The resulting vision system is shown
to significantly boost the performance of standard CNNs across a large and
diverse collection of image data sets. An ensemble of different topologies
using the same approach obtains state-of-the-art results on a virus data set.
