Workspace for image clustering based on empirical mode decomposition

Krinidis S.; Krinidis M.; Chatzis V.

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Workspace for image clustering based on empirical mode decomposition

机译：基于经验模式分解的图像聚类工作空间

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This study presents a new approach for image clustering, which is based on a novel workspace derived from the empirical mode decomposition (EMD). The proposed algorithm exploits the EMD, which can decompose any non-linear and non-stationary data into a number of intrinsic mode functions (IMFs). The intermediate IMFs of the image histogram have very good characteristics and provide a robust workspace that is utilised in order to detect the clusters of an image in a fast way. The proposed method was applied to several images and the obtained results show good image clustering robustness and low computational time, overcoming the disadvantages of the existing image clustering algorithms.

机译：这项研究提出了一种新的图像聚类方法，该方法基于从经验模式分解（EMD）派生的新颖工作空间。所提出的算法利用了EMD，该EMD可以将任何非线性和非平稳数据分解为许多固有模式函数（IMF）。图像直方图的中间IMF具有非常好的特性，并提供了强大的工作空间，可用于快速检测图像的群集。该方法应用于多张图像，获得的结果显示了良好的图像聚类鲁棒性和较低的计算时间，克服了现有图像聚类算法的缺点。

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《Image Processing, IET》 |2012年第6期|p.778-785|共8页
作者
Krinidis S.; Krinidis M.; Chatzis V.;
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Information Management Department, Technological Institute of Kavala;

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Workspace for image clustering based on empirical mode decomposition

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