Cascade Fuzzy Adaptive Hamming Net : A Coarse-to-Fine Representation Scheme for Object Recognition

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

TR-IIS-96-003 (Fulltext)

neural network, unsupervised learning, self organization, hierarchical representation


In this report, we propose a cascade fuzzy adaptive Hamming net (CFAHN) which can function as an extensible database in a model-based object recognition system. The proposed CFAHN can accept both binary and analog inputs. The architecture of a CFAHN not only preserves the prominent characteristics of the FAHN (i.e., parallel pattern matching, fast learning and stable categorization), but also extends its capability to the hierarchical class representation of input patterns. The developed CFAHN is an unsupervised learning neural network, which can be used to store new object categories in an extensible manner. Moreover, every path in the database reveals a coarse-to-fine representation of an input pattern.