Workspace for image clustering based on empirical mode decomposition

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.