  Pattern recognition is generally assumed as an interaction of two inversely
directed image-processing streams: the bottom-up information details gathering
and localization (segmentation) stream, and the top-down information features
aggregation, association and interpretation (recognition) stream. Inspired by
recent evidence from biological vision research and by the insights of
Kolmogorov Complexity theory, we propose a new, just top-down evolving,
procedure of initial image segmentation. We claim that traditional top-down
cognitive reasoning, which is supposed to guide the segmentation process to its
final result, is not at all a part of the image information content evaluation.
And that initial image segmentation is certainly an unsupervised process. We
present some illustrative examples, which support our claims.
