Intelligent machine learning algorithms for colour segmentation

IDOKO JOHN BUSH; RAHIB ABIYEV; MOHAMMAD KHALEEL MAAITAH

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Intelligent machine learning algorithms for colour segmentation

机译：用于彩色分割的智能机器学习算法

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摘要

Skin colour detection has been a commendable technique due to its wide range of application in both analyses based on diagnostic and human computer interactions. Various problems could be solved by simply providing an appropriate method for pixel-like skin parts. Presented in this study is a colour segmentation algorithm that works directly in RGB colour space without converting the colour space. Genfis function as explored in this study formed the Sugeno fuzzy network and utilizing Fuzzy C-Mean (FCM) clustering rule, clustered the data and for each cluster/class a rule is generated. Also, the Radial Basis Function (RBF) utilized Gaussian function for grouping. Finally, corresponding output from data mapping of pseudo-polynomial is obtained from input dataset to the adaptive neuro fuzzy inference system (ANFIS), while the Euclidean distance performed data mapping in the RBF model. The result obtained from these two algorithms depicts the RBFN outperforming ANFIS with remarkable margins.

机译：由于基于诊断和人机交互的分析，肤色检测是一种值得称称的技术。只需为像素样皮肤部件提供适当的方法，可以解决各种问题。本研究呈现的是一种颜色分割算法，可直接在RGB颜色空间中工作，而无需转换色彩空间。本研究中探索的Genfis功能形成了Sugeno模糊网络并利用模糊C-MEAL（FCM）群集规则，群集数据和每个群集/类规则。此外，径向基函数（RBF）利用高斯函数进行分组。最后，从伪多项式的数据映射的对应输出从输入数据集获得到自适应神经模糊推理系统（ANFIS），而欧几里德距离在RBF模型中执行数据映射。从这两个算法获得的结果描述了具有显着的边缘的rBFn优于anfis。

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来源
《WSEAS Transactions on Signal Processing》 |2017年第2期|共9页
作者
IDOKO JOHN BUSH; RAHIB ABIYEV; MOHAMMAD KHALEEL MAAITAH;
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作者单位

Computer Engineering Department Near East University;

Computer Engineering Department Near East University;

Computer Engineering Department Near East University;

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原文格式 PDF
正文语种 eng
中图分类声学;
关键词
Skin; Nonskin; Adaptive neuro fuzzy inference system; Radial basis function;

机译：皮肤;nonskin;自适应神经模糊推理系统;径向基函数;

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Intelligent machine learning algorithms for colour segmentation

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