Name
Abstract | ||
|---|---|---|
Both image compression based on color quantization and image segmentation are two typical tasks in the field of image processing. Several techniques based on splitting algorithms or cluster analyses have been proposed in the literature. Self-organizing maps have been also applied to these problems, although with some limitations due to the fixed network architecture and the lack of representation in hierarchical relations among data. In this paper, both problems are addressed using growing hierarchical self-organizing models. An advantage of these models is due to the hierarchical architecture, which is more flexible in the adaptation process to input data, reflecting inherent hierarchical relations among data. Comparative results are provided for image compression and image segmentation. Experimental results show that the proposed approach is promising for image processing, and the powerful of the hierarchical information provided by the proposed model. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/s11063-012-9266-5 | Neural Processing Letters |
Keywords | Field | DocType |
Image compression,Video segmentation,Self-organization,Hierarchical self-organizing map,Foreground detection | Scale-space segmentation,Pattern recognition,Feature detection (computer vision),Set partitioning in hierarchical trees,Image texture,Computer science,Image processing,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Machine learning,Image compression | Journal |
Volume | Issue | ISSN |
37 | 1 | 1370-4621 |
Citations | PageRank | References |
4 | 0.43 | 19 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Esteban J. Palomo | 1 | 95 | 14.79 |
| Enrique Domínguez | 2 | 133 | 21.24 |
| Rafael M. Luque-Baena | 3 | 40 | 3.15 |
| José Muñoz | 4 | 16 | 2.44 |