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

Zaidan, A. A. and Ahmad, Nurul Nadia and Abdul Karim, Hezerul and Larbani, Moussa and Zaidan, B. B. and Sali, Aduwati (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

[img] Text
Image skin segmentation based on multi-agent learning Bayesian and neural network.pdf
Restricted to Repository staff only

Download (4MB)
Official URL: http://www.sciencedirect.com/science/article/pii/S...

Abstract

Skin colour is considered to be a useful and discriminating spatial feature for many skin detection-related 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 significantly lower average rate for the false negative (FN) and false positive (FP) (i.e. only 1.56% and 0.14% respectively). The experimental results show that multi-agent learning in the skin detector is more efficient than other approaches.

Item Type: Article
Uncontrolled Keywords: Skin detector, Bayesian method, Neural network
Subjects: H Social Sciences > HF Commerce > HF5001-6182 Business > HF5546-5548.6 Office management
Divisions: Faculty of Engineering (FOE)
Depositing User: Ms Suzilawati Abu Samah
Date Deposited: 04 Jun 2014 08:48
Last Modified: 01 Feb 2017 05:13
URI: http://shdl.mmu.edu.my/id/eprint/5546

Actions (login required)

View Item View Item