Name
Abstract | ||
|---|---|---|
In this paper, a novel approach to large-format image seg- mentation is presented, focused on usage in content-based multimedia applications. The proposed framework aims at facilitating the time-efficient segmentation of large-format images while maintaining the high perceptual quality of the segmentation result. For this to be achieved, the employed segmentation algorithm is applied to reduced versions of the large-format images, in order to speed-up its execution, re- sulting in a coarse-grained segmentation mask. The final fine-grained segmentation mask is produced by an enhance- ment stage that involves partial reclassification of the pixels of the original image using a Bayes classifier. As shown by experimental evaluation, this novel scheme provides fast segmentation with high perceptual segmentation quality. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/ICIP.2002.1038136 | Image Processing. 2002. Proceedings. 2002 International Conference |
Keywords | Field | DocType |
Bayes methods,image classification,image colour analysis,image enhancement,image segmentation,Bayes classifier,color images,content-based multimedia applications,enhancement stage,large-format image segmentation,segmentation mask | Computer vision,Scale-space segmentation,Pattern recognition,Segmentation,Computer science,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Region growing,Connected-component labeling,Contextual image classification,Minimum spanning tree-based segmentation | Conference |
Volume | ISSN | Citations |
1 | 1522-4880 | 9 |
PageRank | References | Authors |
0.84 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| V. Mezaris | 1 | 293 | 16.26 |
| Ioannis Kompatsiaris | 2 | 1404 | 197.36 |
| Michael G. Strintzis | 3 | 568 | 54.23 |