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Image skin segmentation based on multi-agent learning Bayesian and neural network

Aos Alaa, Zaidan and Ahmad, Nurul Nadia and Hezerul, Abdul Karim and M. Larbani, Moussa and Bilal Bahaa, Zaidan and Aduwati, Sali (2014) Image skin segmentation based on multi-agent learning Bayesian and neural network. Engineering Applications of Artificial Intelligence , 32. pp. 136-150. ISSN 0952-1976

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Skin colour is considered to be a useful and discriminating spatial feature for many skin detectionrelated applications, but it is not sufficiently robust to address complex image environments because of light-changing conditions, skin-like colours and reflective glass or water. These factors can create major difficulties in face pixel-based skin detectors when the colour feature is used. Thus, this paper proposes a multi-agent learning method that combines the Bayesian method with a grouping histogram (GH) technique and the back-propagation neural network with a segment adjacent-nested (SAN) technique based on the YCbCr and RGB colour spaces, respectively, to improve skin detection performance. The findings from this study have shown that the proposed multi-agent learning for skin detector has produced significant true positive (TP) and true negative (TN) average rates (i.e. 98.44% and 99.86% respectively). In addition, it has achieved a significantlylower averagerate for the false negative (FN) and false positive (FP) (i.e. only 1.56% and 0.14% respectively). The experimental results show that multiagent learning in the skin detector is more efficient than other approaches. & 2014 Elsevier Ltd. All rights reserved

Item Type: Article (Journal)
Additional Information: 3917/41558
Uncontrolled Keywords: Bayesian method; Neural network; Skin detector
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Economics and Management Sciences > Department of Business Administration
Depositing User: Professor Larbani Moussa
Date Deposited: 16 Feb 2015 09:53
Last Modified: 13 Oct 2015 14:02
URI: http://irep.iium.edu.my/id/eprint/41558

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