CFART : A New Multi-Resolutional Adaptive Resonance System for Object Recognition

Hong-Yuan Mark Liao, Hai-Lung Hung, Chwan-Jye Sze, Shing-Jong Lin, Wei-Chung Lin and Kuo-Chin Fan

TR-IIS-96-002 (Fulltext)

neural network, unsupervised learning, self organization, multi-resolutional representation


ln this report, we propose a cascade fuzzy ART (CFART) neural network which can function as an extensible database in a model-based object recognition system. The proposed CFART network contains multiple layers. It preserves the prominent characteristics of a fuzzy ART network and extends fuzzy ART's capability toward hierarchical representation of input patterns. The learning process of the proposed network is unsupervised and self-organizing, and includes a top-down searching process and a bottom-up learning process. The top-down and bottom-up learning processes interact with each other in a closely coupled manner. Basically, the top-down searching guides the bottom-up learning whereas the bottom-up learning influences the top-down searching by changing its searching fuzzy boundary. In addition, a global searching tree is built to speed up the learning and recognition processes. The proposed CFART can accept both binary and analog inputs. With fast learning and categorization capabilities, the proposed network is able to function like an extensible database and to provide an efficient multi-resolutional representation capability for 3D objects. Experimental results, using both synthetic and real 3D data, prove that the proposed method is indeed an efficient and powerful representation scheme.