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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Incorporating Scale Invariance into the Cellular Associative Neural Network
verfasst von : Nathan Burles, Simon O’Keefe, James Austin
Erschienen in: Artificial Neural Networks and Machine Learning – ICANN 2014
Verlag: Springer International Publishing
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This paper describes an improvement to the Cellular Associative Neural Network, an architecture based on the distributed model of a cellular automaton, allowing it to perform scale invariant pattern matching. The use of tensor products and superposition of patterns allows the system to recall patterns at multiple resolutions simultaneously. Our experimental results show that the architecture is capable of scale invariant pattern matching, but that further investigation is needed to reduce the distortion introduced by image scaling.