Name
Abstract | ||
|---|---|---|
We consider the problem of multi-label, supervised image segmentation when an initial labeling of some pixels is given. In this paper, we propose a new generative image segmentation algorithm for reliable multi-label segmentations in natural images. In contrast to most existing algorithms which focus on the inter-label discrimination, we address the problem of finding the generative model for each label. The primary advantage of our algorithm is that it produces very good segmentation results under two difficult problems: the weak boundary problem and the texture problem. Moreover, single-label image segmentation is possible. These are achieved by designing the generative model with the Random Walks with Restart (RWR). Experimental results with synthetic and natural images demonstrate the relevance and accuracy of our algorithm. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-88690-7_20 | ECCV (3) |
Keywords | Field | DocType |
single-label image segmentation,reliable multi-label segmentation,generative image segmentation,difficult problem,random walks,new generative image segmentation,texture problem,generative model,good segmentation result,natural image,weak boundary problem,supervised image segmentation,random walk,image segmentation | Scale-space segmentation,Computer science,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Minimum spanning tree-based segmentation,Computer vision,Pattern recognition,Image texture,Segmentation,Pixel,Machine learning,Generative model | Conference |
Volume | ISSN | Citations |
5304 | 0302-9743 | 67 |
PageRank | References | Authors |
1.95 | 15 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tae Hoon Kim | 1 | 129 | 11.20 |
| Kyoung Mu Lee | 2 | 3228 | 153.84 |
| Sang Uk Lee | 3 | 1879 | 180.39 |