One shot associative memory method for distorted pattern recognition

Authors:
Asad I. Khan;Anang Hudaya Muhamad Amin
Affiliations:
Clayton School of IT, Monash University, Clayton, VIC, Australia;Clayton School of IT, Monash University, Clayton, VIC, Australia
Venue:
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Year:
2007

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Abstract

In this paper, we present a novel associative memory approach for pattern recognition termed as Distributed Hierarchical Graph Neuron (DHGN). DHGN is a scalable, distributed, and one-shot learning pattern recognition algorithm which uses graph representations for pattern matching without increasing the computation complexity of the algorithm. We have successfully tested this algorithm for character patterns with structural and random distortions. The pattern recognition process is completed in one-shot and within a fixed number of steps.