Name
Abstract | ||
|---|---|---|
In this paper, LBGS, a new parallel/distributed technique for Vector Quantization is presented. It derives from the well known LBG algorithm and has been designed for very complex problems where both large data sets and large codebooks are involved. Several heuristics have been introduced to make it suitable for implementation on parallel/distributed hardware. These lead to a slight deterioration of the quantization error with respect to the serial version but a large improvement in computing efficiency. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1016/j.image.2004.10.001 | Signal Processing: Image Communication |
Keywords | Field | DocType |
Clustering,Vector quantization,Unsupervised learning,Parallel,Distributed,Learning | Data set,Data processing,Computer science,Theoretical computer science,Heuristics,Unsupervised learning,Vector quantization,Quantization (signal processing),Cluster analysis,Complex problems | Journal |
Volume | Issue | ISSN |
20 | 1 | 0923-5965 |
Citations | PageRank | References |
8 | 0.59 | 25 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Giuseppe Campobello | 1 | 54 | 11.19 |
| Mirko Mantineo | 2 | 8 | 0.59 |
| Giuseppe Patanè | 3 | 163 | 17.87 |
| M. Russo | 4 | 425 | 37.60 |