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Compression algorithms have been implemented for k-means and k-means++ clustering and applied to thermographic images. The overall algorithm has four stages and are the same for the two algorithms except for the initialization of the centroids. The compression ratio and quality are primarily dependent on the number of clusters used for the algorithm. A MATLAB GUI was developed to run the algorithms and a comparison has been performed with subjective evaluations and objective RMS error, peak SNR and compression ratio metrics. The average compression ratio was 1.3 and 1.6 for the k-means and k-means++ clustering respectively. The k-means++ clustering provides subjectively better visual results than the standard k-means clustering.
Hridoy Biswas,Scott E. Umbaugh,Dominic Marino, andJoseph Sackman
"Comparison of K-means and K-means++ for image compression with thermographic images", Proc. SPIE 11743, Thermosense: Thermal Infrared Applications XLIII, 117430U (12 April 2021); https://doi.org/10.1117/12.2587264
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Hridoy Biswas, Scott E. Umbaugh, Dominic Marino, Joseph Sackman, "Comparison of K-means and K-means++ for image compression with thermographic images," Proc. SPIE 11743, Thermosense: Thermal Infrared Applications XLIII, 117430U (12 April 2021); https://doi.org/10.1117/12.2587264