Cognitive image representation based on spectrum pyramid decomposition

Authors:
Roumen Kountchev;Stuart Rubin;Mariofanna Milanova;Vladimir Todorov;Roumiana Kountcheva
Affiliations:
Department of Radio Communications, Technical University of Sofia, Bulgaria;Space and Naval Warfare Systems Center, San Diego, CA;Department of Computer Science, UALR;T&K Engineering, Sofia, Bulgaria;T&K Engineering, Sofia, Bulgaria
Venue:
MMACTEE'08 Proceedings of the 10th WSEAS International Conference on Mathematical Methods and Computational Techniques in Electrical Engineering
Year:
2008

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Abstract

The contemporary image representation is based on various techniques, using matrices, vectors, multi-resolution pyramids, R-tree, orthogonal transforms, anisotropic perceptual representations, etc. In this paper is offered one new approach for cognitive image representation based on adaptive spectrum pyramid decomposition controlled by neural networks. This approach corresponds to the hypothesis of the human way for image recognition using consecutive approximations with increasing resolution for the selected regions of interest. Such image representation is suitable for the creation of the objects' learning models, which should be extracted from image databases in accordance with predefined decision rules. Significant element of the new representation is the use of a feedback, which to provide iterative change of the cognitive models' parameters in accordance with the data mining results obtained.