Multiscale image segmentation using a hierarchical self-organizing map

Abstract

Multiscale structures and algorithms that unify the treatment of local and global scene information are of particular importance in image segmentation. Vector quantization, owing to its versatility, has proved to be an effective means of image segmentation. Although vector quantization can be achieved using self-organizing maps with competitive learning, self-organizing maps in their original single-layer structure, are inadequate for image segmentation. A hierarchical self-organizing neural network for image segmentation is presented. The Hierarchical Self-Organizing Map (HSOM) is an extension of the conventional (single-layer) Self-Organizing Map (SOM). The problem of image segmentation is formulated as one of vector quantization and mapped onto the HSOM. By combining the concepts of self-organization and topographic mapping with those of multiscale image segmentation the HSOM alleviates the shortcomings of the conventional SOM in the context of image segmentation.