Dynamic shape learning and forgetting

Authors:
Nikolaos Tsapanos;Anastasios Tefas;Ioannis Pitas
Affiliations:
Department of Informatics, Aristotle University of Thessaloniki, Greece and Informatics and Telematics Institute, CERTH;Department of Informatics, Aristotle University of Thessaloniki, Greece;Department of Informatics, Aristotle University of Thessaloniki, Greece and Informatics and Telematics Institute, CERTH
Venue:
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Year:
2010

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Abstract

In this paper, we present a system capable of dynamically learning shapes in a way that also allows for the dynamic deletion of shapes already learned. It uses a self-balancing Binary Search Tree (BST) data structure in which we can insert shapes that we can later retrieve and also delete inserted shapes. The information concerning the inserted shapes is distributed on the tree's nodes in such a way that it is retained even after the structure of the tree changes due to insertions, deletions and rebalances these two operations can cause. Experiments show that the structure is robust enough to provide similar retrieval rates after many insertions and deletions.