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.
The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2007-2013) under Grant agreement No. 211471 (i3DPost).
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References
Borgefors, G.: Distance transformations in digital images. Computer Vision, Graphics, and Image Processing 34(3), 344–371 (1986)
Gavrila, D.M.: A bayesian, exemplar-based approach to hierarchical shape matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(8), 1408–1421 (2007)
Klanderman, G., Huttenlocher, D., Rucklidge, W.J.: Comparing images using the Hausdorff distance. IEEE Trans. Pattern Analysis and Machine Intelligence 15(9), 850–863 (1993)
Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(7), 2278–2324 (1998)
Rucklidge, W.J.: Locating objects using the Hausdorff distance. Proceedings of Fifth International Conference on Computer Vision 146(7), 457–464 (1995)
Tsapanos, N., Tefas, A., Pitas, I.: An online self-balancing binary search tree for hierarchical shape matching. In: VISAPP, vol. (1), pp. 591–597 (2008)
Zhang, D., Lu, G.: Review of shape representation and description techniques. Pattern Recognition 37(1), 1–19 (2004)
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Tsapanos, N., Tefas, A., Pitas, I. (2010). Dynamic Shape Learning and Forgetting. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6354. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15825-4_44
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DOI: https://doi.org/10.1007/978-3-642-15825-4_44
Publisher Name: Springer, Berlin, Heidelberg
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