Optimality of spectral clustering in the Gaussian mixture model

­­Image Segmentation using Gaussian Mixture Model

Abstract: Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we used Gaussian mixture model to the pixels of an image. The parameters of the model were estimated by EM-algorithm.   In addition pixel labeling corresponded to each pixel of true image was made by Bayes rule. In fact,...

IMAGE SEGMENTATION USING GAUSSIAN MIXTURE MODEL

  Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we have learned Gaussian mixture model to the pixels of an image. The parameters of the model have estimated by EM-algorithm.   In addition pixel labeling corresponded to each pixel of true image is made by Bayes rule. In fact, ...

Multivariate Data Clustering for the Gaussian Mixture Model

This paper discusses a soft sample clustering problem for multivariate independent random data satisfying the mixture model of the Gaussian distribution. The theory recommends to estimate the parameters of model by the maximum likelihood method and to use “plug-in” approach for data clustering. Unfortunately, the calculation problem of the maximum likelihood estimate is not completely solved in...

A Gaussian Mixture Model Spectral Representation for Speech Recognition

A Novel Evolutionary Clustering Algorithm Based on Gaussian Mixture Model

Estimating the optimal number of clusters for a dataset is one of the most essential issues in cluster analysis. Traditional clustering algorithms usually predefine the number of clusters via random selection or contend based knowledge. An improper pre-selection for the number of clusters may easily lead to bad clustering outcome. In order to address this issue we propose in this paper a new ev...