Comparison of the Efficiency of K-Means, GMM and EM Algorithms in Image Processing

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This study assesses the efficacy of three widely recognized picture clustering algorithms: K-Means Image processing is a crucial undertaking in various sectors, such as satellite images, surveillance, and medical imaging. Image clustering is the essential process in image processing when pixels with similar characteristics are grouped into clusters. This study assesses the performance of the K-Means Clustering, Gaussian Mixture Model (GMM), and Expectation-Maximization picture clustering algorithms. We evaluate the efficacy of these algorithms by comparing their effectiveness across different industries, taking into account numerous characteristics and their usability. While K-Means Clustering is pragmatic and uncomplicated, it may not yield satisfactory results when applied to images with non-uniformly distributed clusters or clusters of varying sizes. The GMM method exhibits greater flexibility and is capable of effectively processing intricate images of varying dimensions, as well as clusters that are not uniformly dispersed. Computing expenses for this method may exceed those of K-Means Clustering. Despite its increased processing cost, the iterative EM technique is capable of handling images that contain clusters with non-uniform distributions and clusters of varying sizes. This work performs a comparative analysis of various algorithms to assist researchers and practitioners in selecting the most optimal imageprocessing algorithm for a specific application.