Name
Abstract | ||
|---|---|---|
An image segmentation method that does not need training data can provide faster results than methods using complex optimization. Motivated by this idea, we present an unsupervised image segmentation method that combines comparative reasoning with graph-based clustering. Comparative reasoning enables fast similarity search on the image, and these search results are used with the Random Walks algorithm, which is used for clustering and calculating class probabilities. Our method is validated on diverse image modalities such as biomedical images, natural images and texture images. The performance of the method is measured through cluster purity based on available ground truth. Our results are compared to existing segmentation methods using Global Consistency Error scores. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/GlobalSIP.2015.7418213 | 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP) |
Keywords | Field | DocType |
Unsupervised image segmentation,hashing,Winner Take All hash,comparative reasoning,Random Walks | Computer vision,Scale-space segmentation,Pattern recognition,Image texture,Segmentation,Segmentation-based object categorization,Image segmentation,Region growing,Artificial intelligence,Cluster analysis,Minimum spanning tree-based segmentation,Mathematics | Conference |
Citations | PageRank | References |
0 | 0.34 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anuva Kulkarni | 1 | 5 | 1.28 |
| Filipe Condessa | 2 | 20 | 4.52 |
| Jelena Kovacevic | 3 | 802 | 95.87 |