Name
Title | ||
|---|---|---|
Hierarchical classification-based region growing (HCBRG): a collaborative approach for object segmentation and classification |
Abstract | ||
|---|---|---|
Object-based image classification approaches heavily rely on the segmentation process. However, the lack of interaction between both segmentation and classification steps is one of the major limits of these approaches. In this paper, we introduce a hierarchical classification based on a region growing approach driven by expert knowledge represented in a concept hierarchy. In order to overcome the region growing's limits, a first classification will associate a confidence score to each region in the image. This score will be used through an iterative step, which allows interaction between segmentation and classification at each iteration. Carried out experiments on a Quickbird image show the benefits of the introduced approach. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-31295-3_7 | ICIAR |
Keywords | Field | DocType |
concept hierarchy,object segmentation,iterative step,confidence score,object-based image classification,quickbird image,classification step,collaborative approach,segmentation process,hierarchical classification,region growing,hierarchical classification-based region,expert knowledge,segmentation,classification,collaboration | Confidence score,Computer vision,Scale-space segmentation,Pattern recognition,Computer science,Segmentation,Segmentation-based object categorization,Region growing,Artificial intelligence,Contextual image classification,Concept hierarchy | Conference |
Volume | ISSN | Citations |
7324 | 0302-9743 | 4 |
PageRank | References | Authors |
0.61 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aymen Sellaouti | 1 | 6 | 1.72 |
| Atef Hamouda | 2 | 40 | 12.57 |
| Aline Deruyver | 3 | 77 | 11.56 |
| Cédric Wemmert | 4 | 96 | 15.05 |