Name
Abstract | ||
|---|---|---|
This paper introduces an unsupervised color segmentation method. The underlying idea is to segment the input image several times, each time focussing on a different salient part of the image and to subsequently merge all obtained results into one composite segmentation. We identify salient parts of the image by applying affinity propagation clustering to efficiently calculated local color and texture models. Each salient region then serves as an independent initialization for a figure/ground segmentation. Segmentation is done by minimizing a convex energy functional based on weighted total variation leading to a global optimal solution. Each salient region provides an accurate figure/ ground segmentation highlighting different parts of the image. These highly redundant results are combined into one composite segmentation by analyzing local segmentation certainty. Our formulation is quite general, and other salient region detection algorithms in combination with any semi-supervised figure/ground segmentation approach can be used. We demonstrate the high quality of our method on the well-known Berkeley segmentation database. Furthermore we show that our method can be used to provide good spatial support for recognition frameworks. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICCV.2009.5459296 | Kyoto |
Keywords | DocType | Volume |
image colour analysis,image recognition,image segmentation,image texture,pattern clustering,Berkeley segmentation database,affinity propagation clustering,composite segmentation,convex energy,figure segmentation,ground segmentation,local color model,recognition framework,saliency driven total variation segmentation,salient region detection algorithm,texture model,unsupervised color segmentation method,weighted total variation | Conference | 2009 |
Issue | ISSN | ISBN |
1 | 1550-5499 E-ISBN : 978-1-4244-4419-9 | 978-1-4244-4419-9 |
Citations | PageRank | References |
45 | 2.18 | 29 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Donoser | 1 | 617 | 31.10 |
| Martin Urschler | 2 | 347 | 23.94 |
| Martin Hirzer | 3 | 592 | 18.74 |
| Horst Bischof | 4 | 8751 | 541.43 |