Gaussian Mixture Models Tutorial
Published:
Theory and implementation of Gaussian Mixture Models and the Expectation Maximization Algorithm.
Description:
This .PDF technical report describes the mathematics theory and programming implementation of Gaussian Mixture Models and Expectation Maximization applied to image segmentation. I found myself needing to implement an image segmentation algorithm, but did not have the requisite training data to reproduce state-of-the-art methods in the domain. This motivated me to study the application of Gaussian Mixture Models, a classical machine learning approach that - while unable to produce state-of-the-art results - is fully unsupervised and does not require training data. The accompanying Python reference implementation is available on my GitHub page.
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