Abstract
We propose a non-parametric clustering algorithm for 1-dimensional data inspired by Occam’s Razor. The procedure looks for the simplest (i.e. smoothest) density that is still compatible with the data. Compatibility is given a precise meaning in terms of distribution-free statistics based on the empirical distribution function. We apply this algorithm to image-segmentation based on data-driven 1-dimensional colour-spaces.
— Entities must not be multiplied beyond what is necessary —
William of Occam (1284 – 1347)
— Everything should be made as simple as possible, but not simpler —
Albert Einstein (1879 – 1955)
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Frederix, G., Pauwels, E.J. (2001). Distribution-Free Statistics for Segmentation. In: Veltkamp, R.C., Burkhardt, H., Kriegel, HP. (eds) State-of-the-Art in Content-Based Image and Video Retrieval. Computational Imaging and Vision, vol 22. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-9664-0_8
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DOI: https://doi.org/10.1007/978-94-015-9664-0_8
Publisher Name: Springer, Dordrecht
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