Authors
Obada Al aama1, Hakaru Tamukoh2, *
1Department of Life Science and Systems Engineering, Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, 2-4 Hibikino, Wakamatsu-ku, Kitakyushu 808-0196, Japan
2Department of Human Intelligence System, Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, 2-4 Hibikino, Wakamatsu-ku, Kitakyushu 808-0196, Japan
*Corresponding author. Email: [email protected]
Corresponding Author
Hakaru Tamukoh
Received 11 November 2019, Accepted 25 February 2020, Available Online 20 May 2020.
DOI
https://doi.org/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 ALife Robotics Corp. Ltd.
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/).