Synergetic learning for unsupervised texture classification tasks

Synergetic computers form a class of self-organized algorithms. Due to their close similarity to nonlinear self-organized systems In physics and chemistry they are potential candidates for a new sort of image processing hardware. We will study the performance of an unsupervised synergetic learning algorithm with classification problems on both artificial and real texture data and will show that unsupervised synergetic learning can be successfully used for unsupervised pattern classification.