Training Autoencoder using Three Different Reversed Color Models for Anomaly Detection

DOI

10.2991/jrnal.k.200512.008How to use a DOI?

Keywords

Convolutional neural network; autoencoder; anomaly detection; color models

Abstract

Autoencoders (AEs) have been applied in several applications such as anomaly detectors and object recognition systems. However, although the recent neural networks have relatively high accuracy but sometimes false detection may occur. This paper introduces AE as an anomaly detector. The proposed AE is trained using both normal and anomalous data based on convolutional neural network with three different color models Hue Saturation Value (HSV), Red Green Blue (RGB), and our own model (TUV). As a result, the trained AE reconstruct the normal images without change, whereas the anomalous image would be reconstructed reversely. The training and testing of the AE in case of RGB, HSV, and TUV color models were demonstrated and Cifar-10 dataset had been used for the evaluation process. It can be noticed that HSV color model has been more effective and achievable as an anomaly detector rather than other color models based on Z- and F-test analyses.

Copyright

© 2020 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Obada Al aama
AU  - Hakaru Tamukoh
PY  - 2020
DA  - 2020/05/20
TI  - Training Autoencoder using Three Different Reversed Color Models for Anomaly Detection
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 35
EP  - 40
VL  - 7
IS  - 1
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.k.200512.008
DO  - 10.2991/jrnal.k.200512.008
ID  - Alaama2020
ER  -