In this paper, we propose a novel approach of vector quantization using a merge-based hierarchical neural network. Vector quantization¡]VQ¡^is known as a very useful technique for lossy data compression. Recently, Neural network¡]NN¡^algorithms have been used for VQ. Vlajic and Card proposed a modified adaptive resonance theory (modified ART2¡^[1] which is a constructing tree structure clustering method. However, modified ART2 has disadvantages of slow construction rate and constructing many redundant levels. Therefore, we propose a more efficient approach for constructing the tree in this paper. Our method establishes only those required levels without losing the fidelity of a compressed image.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0902103-163028 |
| Date | 02 September 2003 |
| Creators | CHUNG, JUN-SHIH |
| Contributors | Shie-Jue Lee, Chih-Hung Wu, Tzung-Pei Hong, Been-Chian Chien, Chaur-Heh Hsieh |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | Cholon |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0902103-163028 |
| Rights | unrestricted, Copyright information available at source archive |
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