Title

Skin Segmentation using Color Pixel Classification: Analysis and Comparison

Document Type

Journal Article

Publisher

IEEE Computer Society

Faculty

Computing, Health and Science

School

School of Computer and Information Science, Centre for Communications Engineering Research

RAS ID

3482

Comments

This article was originally published as: Phung, S. L., Bouzerdoum, A., & Chai, D. K. (2005). Skin segmentation using color pixel classification: analysis and comparison. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(1), 148-154. Original article available here

Abstract

This work presents a study of three important issues of the color pixel classification approach to skin segmentation: color representation, color quantization, and classification algorithm. Our analysis of several representative color spaces using the Bayesian classifier with the histogram technique shows that skin segmentation based on color pixel classification is largely unaffected by the choice of the color space. However, segmentation performance degrades when only chrominance channels are used in classification. Furthermore, we find that color quantization can be as low as 64 bins per channel, although higher histogram sizes give better segmentation performance. The Bayesian classifier with the histogram technique and the multilayer perceptron classifier are found to perform better compared to other tested classifiers, including three piecewise linear classifiers, three unimodal Gaussian classifiers, and a Gaussian mixture classifier.

DOI

10.1109/TPAMI.2005.17

Access Rights

free_to_read

 

Link to publisher version (DOI)

10.1109/TPAMI.2005.17