Name
Abstract | ||
|---|---|---|
Unsupervised, automatic image segmentation without contextual knowledge, or user intervention is a challenging problem. The key to robust segmentation is an appropriate selection of local features and metrics. However, a single aggregation of the local features using a greedy merging order often results in incorrect segmentation. This paper presents an unsupervised approach, which uses the consensus inferred from hierarchical segmentation ensembles, for partitioning images into foreground and background regions. By exploring an expanded set of possible aggregations of the local features, the proposed method generates meaningful segmentations that are not often revealed when only the optimal hierarchy is considered. A graph cuts-based approach is employed to combine the consensus along with a foreground-background model estimate, obtained using the ensemble, for effective segmentation. Experiments with a standard dataset show promising results when compared to several existing methods including the state-of-the-art weak supervised techniques that use co-segmentation. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICIP.2014.7025662 | ICIP |
Keywords | Field | DocType |
multiple hierarchies,graph cuts,hierarchical segmentation ensemble consensus,superpixels,consensus clustering,foreground region,cosegmentation,unsupervised segmentation,image segmentation,graph cuts-based approach,contextual knowledge,user intervention,weak supervised technique,background region,greedy merging order,feature extraction,feature selection,image partitioning | Cut,Computer vision,Scale-space segmentation,Pattern recognition,Segmentation,Computer science,Segmentation-based object categorization,Image segmentation,Consensus clustering,Artificial intelligence,Region growing,Minimum spanning tree-based segmentation | Conference |
ISSN | Citations | PageRank |
1522-4880 | 3 | 0.40 |
References | Authors | |
13 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hyojin Kim | 1 | 22 | 2.66 |
| Jayaraman J. Thiagarajan | 2 | 247 | 42.17 |
| Peer-Timo Bremer | 3 | 1446 | 82.47 |