In this article, the *clustering output results* using ** GMM-EM soft clustering** is going to be compared with that of

**on an image.**

*KMeans hard clustering*- The
*apples and oranges*image shown below is used for the comparing the clustering techniques. - The two color channels R,G are used as the variables for this image data.
- Two initial
*2-dimensional Gaussian models*the first one with red (1,0) and the second one with green (0,1) mean vectors along with random covariance matrices are used as initial models for*GMM-EM*. - The same two initial points (1,0) and (0,1) are used as the initial
*cluster centroids*for the*Kmeans clustering*also. - The
**EM**algorithm steps for GMM and change in the*Gaussian*contours with iterations (till convergence) are shown in the next animation. - The change in the
*centroids*with iteration (till convergence) for the*KMeans clustering*are shown in the next animation. - Finally, after both the algorithms converge, the pixels assigned to one of clusters obtained are marked as black, for each of the algorithms. The next figure shows that
**GMM-EM**can identify the orange from the apples but**KMeans**can not.

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