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.
Index Terms—3D histogram, color space, hillclimbing segmentation with k-means, particle swarm optimization, PSNR.
R. Vijayanandh is with the Development Centre, Bharathiar University, Coimbatore, Tamil Nadu, India (e-mail: rvanandh@gmail.com).
G. Balakrishnan is with the Department of Computer Science and Engineering, Indra Ganesan College of Engineering, Tiruchirappalli, TamilNadu, India (e-mail: balakrishnan.g@gmail.com).
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.
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