Name
Abstract | ||
|---|---|---|
A simple and successful design of initial codebook of vector quantization (VQ) is presented. For existing initial codebook algorithms, such as random method, the initial codebook is strongly influenced by selection of initial codewords and difficult to match with the features of the training vectors. In the proposed method, training vectors are sorted according to the norm of training vectors. Then, the ordered vectors are partitioned into N groups where N is the size of codebook. The initial codewords are obtained from calculating the centroid of each group. This initializtion method has a robust performance and can be combined with the VQ algorithm to further improve the quality of codebook. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1093/ietisy/e91-d.8.2189 | IEICE Transactions |
Keywords | Field | DocType |
vector quantizaton,n group,initial codebook,initial codebook algorithm,robust performance,random method,initializtion method,initial codewords,vq algorithm,training vector,image processing,vector quantization,computational complexity | Linde–Buzo–Gray algorithm,Pattern recognition,Computer science,Algorithm,Image processing,Vector quantization,Artificial intelligence,Data compression,Centroid,Computational complexity theory,Codebook | Journal |
Volume | Issue | ISSN |
E91-D | 8 | 1745-1361 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| ShanXue Chen | 1 | 0 | 1.01 |
| Fangwei Li | 2 | 23 | 12.01 |
| Weile Zhu | 3 | 43 | 5.94 |
| Tianqi Zhang | 4 | 68 | 21.52 |