The remarkable successes achieved by Deep Learning (DL) in computer vision
drew the attention of researchers to exploit this capability to apply it for the
processing of time series and multivariate data. Thus, researcher began the
development of suitable methods to transform time series signals into, images to
enable the use of DL techniques to improve the classification of time series data.
The most state-of-art techniques are Recurrence Plot (RP), Gramian Angular Field
(GAF), or Markov Transition Field (MTF). These techniques transform each time
series into RGB image. In this paper, a new transformation technique of time series
and multivariate data to images technique is proposed. This technique is called
<Grayscale Fingerprint Features Field Imaging" (G3FI). The main differences
between this technique and state-of-art techniques are: a) the resulted image is a
grayscale; b) the size of the resulted image is much smaller than the size of resulted
images using state-of-art techniques. These differences provide the proposed
technique with several advantages over the prior art, as it (a) results in avoiding
redundant information and (b) noise, and (c) leads to a significant reduction in the
required computational power. For the proof of concept, a dataset "Sonar, Mines vs.
Rocks" is investigated and individually transformed using RP, GAF, and MTF, and
the new developed technique "G3FI". The resulted images from each transformer
are individually used to train and test Convolutional Neural Networks (CNN). The
proposed method leads to competitive results in comparison to RP, GAF, and MTF.