The mean shift algorithm and the unified framework

This paper considers two classes of algorithms for the representation of data points using centroids: the unified framework and the mean shift algorithm. The relationship between both approaches is presented showing that the mean shift algorithm fits within the unified framework being equivalent to snake with Cohen potential. However it does not use competitive learning as the other methods considered in the unified framework. The advantages of both types of techniques are exemplified through examples.