Abstract—In histogram-based image segmentation, the AsymmetricGeneralized Mixture Model (AGGMM) is a powerful toolto fit accurately the real images histograms by handling, amongothers, any asymmetry of the modes. However, the ExpectationMaximization (EM) algorithm, used for the estimation of themixture model parameters, is known to be very sensitive tostarting conditions and can lead to erroneous segmentationresults when the initialization is not adequate. In this paper, wepropose a new method to initialize the AGGMM. This methodis based on geometrical aspects of the histogram. First experimentationsimplying synthetic images generated by AsymmetricGeneralized Mixture Distribution (AGGD) model, reveal a goodrecovering of the input mixture parameters when applying theproposed method. Second experimentations involving real-worldimages have shown, how the initial parameters computed bythe proposed method permit to achieve better histogram fittingwith less EM algorithm running time in comparison to otherinitialization methods.
