A novel workspace for image clustering

M. Krinidis, S. Krinidis, V. Chatzis
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Abstract

A novel image clustering method based on the image histogram, which is processed by the empirical mode decomposition (EMD) is presented. An intermediate step derived from the EMD, which can decompose any nonlinear and non-stationary data into a number of intrinsic mode functions (IMFs) is exploited. The IMFs of the image histogram have interesting characteristics and provide a novel workspace that is utilized in order to automatically detect the different clusters into the image under examination. The proposed method was applied to several real and synthetic images and the obtained results show good image clustering robustness.
一种新的图像聚类工作空间
提出了一种基于图像直方图的图像聚类方法,并对其进行经验模态分解(EMD)处理。从EMD中衍生出一个中间步骤,它可以将任何非线性和非平稳数据分解为许多内禀模态函数(IMFs)。图像直方图的imf具有有趣的特征,并提供了一个新的工作空间,用于自动检测被检查图像中的不同簇。将该方法应用于真实图像和合成图像,结果表明该方法具有较好的聚类鲁棒性。
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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