Comparison of Hebbian learning methods for image compression using the mixture of principal components network

Robert D. Dony

doi:10.1117/12.304660

1 April 1998 Comparison of Hebbian learning methods for image compression using the mixture of principal components network

Robert D. Dony

Author Affiliations +

Proceedings Volume 3307, Applications of Artificial Neural Networks in Image Processing III; (1998) https://doi.org/10.1117/12.304660
Event: Photonics West '98 Electronic Imaging, 1998, San Jose, CA, United States

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.

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Robert D. Dony "Comparison of Hebbian learning methods for image compression using the mixture of principal components network", Proc. SPIE 3307, Applications of Artificial Neural Networks in Image Processing III, (1 April 1998); https://doi.org/10.1117/12.304660

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