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Proceedings Paper

Tree-structured vector quantization with input-weighted distortion measures
Author(s): Pamela C. Cosman; Karen Oehler; Amanda A. Heaton; Robert M. Gray
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Paper Abstract

A greedy tree-growing algorithm is used in conjunction with an input-dependent weighted distortion measure to develop a tree-structured vector quantizer. Vectors in the training set are classified, and weights are assigned to the classes. The resulting weighted distortion measure forces the tree to develop better representations for those classes that are considered important. Results on medical images and USC database images are presented. A tree-structured vector quantizer grown in a similar manner can be used for preliminary classification as well as compression.

Paper Details

Date Published: 1 November 1991
PDF: 10 pages
Proc. SPIE 1605, Visual Communications and Image Processing '91: Visual Communication, (1 November 1991); doi: 10.1117/12.50308
Show Author Affiliations
Pamela C. Cosman, Stanford Univ. (United States)
Karen Oehler, Stanford Univ. (United States)
Amanda A. Heaton, Stanford Univ. (United States)
Robert M. Gray, Stanford Univ. (United States)

Published in SPIE Proceedings Vol. 1605:
Visual Communications and Image Processing '91: Visual Communication
Kou-Hu Tzou; Toshio Koga, Editor(s)

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