Abstract
This chapter deals with some problems of using clustering techniques K-means (KM) and K-harmonic means (KHM) in colour image quantisation. A lot of attention has been paid to initialisation procedures, because they strongly affect the results of the quantisation. Classical versions of KM and KHM start with randomly selected centres. Authors are more interested in using deterministic initialisations based on the distribution of image pixels in the colour space. In addition to two previously proposed initialisations (DC and SD), here is considered a new outlier-based initialisation. It is based on the modified Mirkin’s algorithm (MM) and places the cluster centres in peripheral (outlier) colours of pixels cloud. New approach takes into account small clusters, sometimes representing colours important for proper perception of quantised image. Pixel clustering was created in the RGB, YCbCr and CIELAB colour spaces. Finally, resulting quantised images were evaluated by means of average colour differences in RGB (PSNR) and CIELAB (\( \Delta E\)) colour spaces and additionally by the loss of colourfulness (\(\Delta M\)).
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Acknowledgements
This work was supported by Polish Ministry for Science and Higher Education under internal grant BK-/RAu1/2014 for Institute of Automatic Control, Silesian University of Technology, Gliwice, Poland.
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Palus, H., Frackiewicz, M. (2015). Colour Image Quantisation using KM and KHM Clustering Techniques with Outlier-Based Initialisation. In: Tavares, J., Natal Jorge, R. (eds) Developments in Medical Image Processing and Computational Vision. Lecture Notes in Computational Vision and Biomechanics, vol 19. Springer, Cham. https://doi.org/10.1007/978-3-319-13407-9_16
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DOI: https://doi.org/10.1007/978-3-319-13407-9_16
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