In this paper, we introduce a neural network-based shape matching algorithm that uses Johnson Counter codes coupled with chain codes. Shape matching is a fundamental requirement in content-based image retrieval systems. Chain codes describe shapes using sequences of numbers. They are simple and flexible. We couple this power with the efficiency and flexibility of a binary associative-memory neural network. We focus on the implementation details of the algorithm when it is constructed using the neural network. We demonstrate how the binary associative-memory neural network can index and match chain codes where the chain code elements are represented by Johnson codes.
|Copyright, Publisher and Additional Information:||See also http://eprints.whiterose.ac.uk/5431/|
|Keywords:||Neural,Associative Memory,Shape Matcher,BinaryEncoding|
|Institution:||The University of York|
|Academic Units:||The University of York > Computer Science (York)|
|Depositing User:||Dr Victoria Hodge|
|Date Deposited:||08 Sep 2008 15:28|
|Last Modified:||06 Feb 2017 15:22|