doi: 10.7763/IJCTE.2012.V4.594
Performance Measure of Human Skin Region Detection Based on Hybrid Particle Swarm Optimization
R. Vijayanandh1
,
G. Balakrishnan2
- 1Development Centre, Bharathiar University, Coimbatore, Tamil Nadu, India.
- 2Department of Computer Science and Engineering, Indra Ganesan College of Engineering, Tiruchirappalli, TamilNadu, India.
Abstract
This paper proposes an application of human skin region detection using Hybrid Particle Swarm Optimization (HPSO) algorithm. It consists of two steps. In the first step, the input RGB color image is converted into CIEL*a*b color space. Then this is clustered by the Hillclimbing segmentation with K-Means clustering algorithm, which will be useful to find the number of clusters and the local optimal solutions. In the second step, these local solutions are further improved by PSO algorithm using YCbCr explicit skin color conditions in order to find the global solution. This solution helps to detect the robust skin region. Finally the performance measure named Peak Signal-to-Noise Ratio (PSNR) is performed on the ground-truth skin dataset. The experimental results has shown the efficiency of the proposed method.
Keywords
- 3D histogram
- color space
- hillclimbing segmentation with k-means
- particle swarm optimization
- PSNR
How to Cite
R. Vijayanandh and G. Balakrishnan, "Performance Measure of Human Skin Region Detection Based on Hybrid Particle Swarm Optimization," International Journal of Computer Theory and Engineering, vol. 4, no. 5, pp. 857-861, 2012. https://doi.org/10.7763/IJCTE.2012.V4.594
Copyright & License
Copyright © 2012 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).