Self-Organizing Maps for Spatial and Temporal AR Models

Abstract

A method is given for unsupervised segmentation and classification of 1D and 2D signals. The method is based on a self-organizing map of "neural" units, like Kohonen's feature map, where every unit represents an AR model with its reference vector. The map will self-organize during an unsupervised learning phase. Several training segments of the signals are presented to the map, and each unit will learn to model di#erent parts of the signals. The results indicate that the self-organizing AR map can learn to distinguish textures from images with unsupervised learning, which makes it suitable for segmentation of an image into di#erent texture classes.