  Clustering in image analysis is a central technique that allows to classify
elements of an image. We describe a simple clustering technique that uses the
method of similarity matrices. We expand upon recent results in spectral
analysis for Gaussian mixture distributions, and in particular, provide
conditions for the existence of a spectral gap between the leading and
remaining eigenvalues for matrices with entries from a Gaussian mixture with
two real univariate components. Furthermore, we describe an algorithm in which
a collection of image elements is treated as a dynamical system in which the
existence of the mentioned spectral gap results in an efficient clustering.
