  In this paper we present an unconventional image segmentation approach which
is devised to meet the requirements of image understanding and pattern
recognition tasks. Generally image understanding assumes interplay of two
sub-processes: image information content discovery and image information
content interpretation. Despite of its widespread use, the notion of "image
information content" is still ill defined, intuitive, and ambiguous. Most
often, it is used in the Shannon's sense, which means information content
assessment averaged over the whole signal ensemble. Humans, however,rarely
resort to such estimates. They are very effective in decomposing images into
their meaningful constituents and focusing attention to the perceptually
relevant image parts. We posit that following the latest findings in human
attention vision studies and the concepts of Kolmogorov's complexity theory an
unorthodox segmentation approach can be proposed that provides effective image
decomposition to information preserving image fragments well suited for
subsequent image interpretation. We provide some illustrative examples,
demonstrating effectiveness of this approach.
