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Proceedings Paper

Texture classification using wavelet preprocessing and vector quantization
Author(s): Eric P. Lam
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Paper Abstract

In this paper, we will discuss a technique of texture image classification using a wavelet decomposition with selective wavelet packet node decomposition. This new approach uses a two-channel wavelet decomposition which is extended to two dimensions. Using the strength as a metric, selective wavelet decomposition is controlled. The metric is used to allow further decomposition or to terminate recursive decompositions. Decision of continuing further decompositions is based on each subband's strength with respect to the strengths of other subbands of the same wavelet decomposition level. Once the decompositions stop, the structure of the packet is stored in a data structure. Using the information from the data structure, dominating channels are extracted. These are defined as paths from the root of the packet to the leaf with the highest strengths. The list of dominating channels are used to train a learning vector quantization neural network.

Paper Details

Date Published: 9 April 2007
PDF: 9 pages
Proc. SPIE 6576, Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks V, 65760I (9 April 2007); doi: 10.1117/12.720008
Show Author Affiliations
Eric P. Lam, Thales Raytheon Systems (United States)


Published in SPIE Proceedings Vol. 6576:
Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks V
Harold H. Szu; Jack Agee, Editor(s)

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