A twin support vector machine based approach to classifying human skin

M. Chandra, S. S. Bedi
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引用次数: 2

Abstract

The classification of color image that contain skin pixel is a challenging process. It is challenging due to certain factor such as brightness, background similar to human skin represent the obstacles thereof. The basic approach of skin detection is color based classification. The human skin image is the composition of RGB color. The data values of skin is calculated by randomly sampling RGB color values of different face images which have different age, race, genders sets. However, its performance has not really been high because of the high overlapped degree between “skin” and “non-skin” pixels. This paper describes the linear norm fuzzy based twin support vector machine (LN-FTSVM)) approach for discriminate skin and non-skin data values of skin dataset and to enhance the skin recognition performance. The concept of fuzzy is resolved the unclassified and overlapped data region problems. If no decision function is positive for a data set, this data set is classified into a class with the large membership value. By computational experiments shows that Experiments result shows that the accuracy is improved by linear norm fuzzy based TSVM (LN-FTSVM)) over the conventional methods.
基于双支持向量机的人体皮肤分类方法
包含皮肤像素的彩色图像分类是一个具有挑战性的过程。由于某些因素,如亮度、与人类皮肤相似的背景等,这是具有挑战性的。皮肤检测的基本方法是基于颜色的分类。人体皮肤图像是由RGB颜色组成的。皮肤的数据值是通过随机抽取不同年龄、种族、性别集合的人脸图像的RGB颜色值来计算的。然而,由于“蒙皮”和“非蒙皮”像素之间的高度重叠,它的性能并不是很高。本文提出了一种基于线性范数模糊的双支持向量机(LN-FTSVM)方法,用于区分皮肤数据集的皮肤和非皮肤数据值,提高皮肤识别性能。模糊的概念解决了数据区域未分类和重叠的问题。如果一个数据集没有决策函数是正的,则该数据集被分类为具有较大隶属度值的类。通过计算实验表明,基于线性范数模糊的TSVM (LN-FTSVM)比传统方法具有更高的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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