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Lecture 3-4 Clustering (1hr) Gaussian Mixture and EM (1hr)

Lecture 3-4 Clustering (1hr) Gaussian Mixture and EM (1hr). Tae-Kyun Kim. Vector Clustering. Pixel Clustering (Image Quantisation). R G B. ``. Patch Clustering. or raw pixels. …. dimension D. ……. ……. K codewords. …. Refer to Lecture 9-10 for BoW. Image Clustering. …….

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Lecture 3-4 Clustering (1hr) Gaussian Mixture and EM (1hr)

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  1. Lecture 3-4Clustering (1hr)Gaussian Mixture and EM (1hr) Tae-Kyun Kim

  2. Vector Clustering

  3. Pixel Clustering (Image Quantisation) R G B ``

  4. Patch Clustering or raw pixels … dimension D …… …… K codewords …

  5. Refer to Lecture 9-10 for BoW

  6. Image Clustering ……

  7. K-means vs GMM Hard clustering Soft clustering

  8. Matrix and Vector Derivatives

  9. K-means Clustering

  10. Statistical Pattern Recognition Toolbox for Matlab http://cmp.felk.cvut.cz/cmp/software/stprtool/ …\stprtool\probab\cmeans.m …\stprtool\probab\cmeans_tk.m

  11. Mixture of Gaussians

  12. Maximum Likelihood

  13. Statistical Pattern Recognition Toolbox for Matlab http://cmp.felk.cvut.cz/cmp/software/stprtool/ …\stprtool\visual\pgmm.m …\stprtool\demos\demo_emgmm.m

  14. Advanced topic (optional) http://www.iis.ee.ic.ac.uk/~tkkim/mlcv/lecture_clustering_em.pdf

  15. EM Algorithm in General

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