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.
@article{arxiv.cs/0503001,
title = {Top-Down Unsupervised Image Segmentation (it sounds like oxymoron, but actually it is not)},
author = {Emanuel Diamant},
journal= {arXiv preprint arXiv:cs/0503001},
year = {2007}
}