Self-organization grouping for feature extraction and image segmentation

Feature extraction and image segmentation (FEIS) are two first goals of almost all image understanding systems. We think of FEIS as a multi-level process of recurrently grouping and describing at each abstraction level. We emphasize the role of grouping during this process because we believe that many features and events in real images are only perceived owing to the combination of weak evidence of several organized pixels or other low-level features. We utilize self-organizing networks to develop grouping systems which take perceptual organization of human visual perception into consideration. We demonstrate our approach by solving two concrete problems of extracting linear features in digital images and partitioning color images into regions.<
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