Generalized multi-layer kohonen network and its application to texture recognition

Abstract

In the paper a multi-layer neural network and its application to texture segmentation is presented. The generalized network is built using two types of elements: CU - clustering units and DCB - data completion blocks. Clustering units are composed of Kohonen networks. Each Kohonen network is a self-organizing map (SOM) trained to be able to distinguish, in an unsupervised way, certain clusters in the input data. Data completion blocks are placed between CU and their aim is to prepare data for the CU. This paper presents a sample application of a double-layer network to automatic texture segmentation. The method has been evaluated on both artificial and real images, and the results achieved are presented.