Name
Abstract | ||
|---|---|---|
In this work we propose a recurrent multivalued network, generalizing Hopfield's model, which can be interpreted as a vector quantifier. We explain the model and establish a relation between vector quantization and sum-of-squares clustering. To test the efficiency of this model as vector quantifier, we apply this new technique to image compression. Two well-known images are used as benchmark, allowing us to compare our model to standard competitive learning. In our simulations, our new technique clearly outperforms the classical algorithm for vector quantization, achieving not only a better distortion rate, but even reducing drastically the computational time. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11840930_62 | ICANN (2) |
Keywords | Field | DocType |
classical algorithm,vector quantization,vector quantifier,distortion rate,computational time,recurrent discrete network,standard competitive learning,recurrent multivalued network,image compression,generalizing hopfield,new technique,competitive learning,sum of squares | Competitive learning,Linde–Buzo–Gray algorithm,Computer science,Vector quantization,Artificial intelligence,Cluster analysis,Pattern recognition,Learning vector quantization,Algorithm,Data compression,Machine learning,Image compression,Lossless compression | Conference |
Volume | ISSN | ISBN |
4132 | 0302-9743 | 3-540-38871-0 |
Citations | PageRank | References |
5 | 0.46 | 17 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Domingo López-Rodríguez | 1 | 55 | 9.24 |
| Enrique Mérida-Casermeiro | 2 | 30 | 5.80 |
| Juan M. Ortiz-De-Lazcano-Lobato | 3 | 13 | 3.00 |
| Ezequiel López-Rubio | 4 | 323 | 39.73 |