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Proceedings Paper

Comparison of Hebbian learning methods for image compression using the mixture of principal components network
Author(s): Robert D. Dony
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Paper Abstract

A number of novel adaptive image compression methods have been developed using a new approach to data representation, a mixture of principal components (MPC). MPC, together with principal component analysis and vector quantization, form a spectrum of representations. The MPC network partitions the space into a number of regions or subspaces. Within each subspace the data are represented by the M principal components of the subspace. While Hebbian learning has been effectively used to extract principal components for the MPC, its stability is still a concern in practice. As a result, computationally more expensive methods such as batch eigendecomposition have produced more consistent results. This paper compares the performance of a number of Hebbian- based training schemes for the MPC network. These include training the entire network, network growing techniques, and a new tree-structured method. In the new tree-structured approach, each level in the tree, M, corresponds to an M- dimensional representation. A node and all its M - 1 parents represents a single M-dimensional subspace or class. The evaluation shows that the use of tree-structured approach improves training and results in reduced squared error.

Paper Details

Date Published: 1 April 1998
PDF: 12 pages
Proc. SPIE 3307, Applications of Artificial Neural Networks in Image Processing III, (1 April 1998); doi: 10.1117/12.304660
Show Author Affiliations
Robert D. Dony, Univ. of Guelph (Canada)


Published in SPIE Proceedings Vol. 3307:
Applications of Artificial Neural Networks in Image Processing III
Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

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