Name
Abstract | ||
|---|---|---|
Most state-of-the-art approaches to image segmentation formulate the problem using Conditional Random Fields. These models typically include a unary term and a pairwise term, whose parameters must be carefully chosen for optimal performance. Recently, structured learning approaches such as Structured SVMs (SSVM) have made it possible to jointly learn these model parameters. However, they have been limited to linear kernels, since more powerful non-linear kernels cause the learning to become prohibitively expensive. In this paper, we introduce an approach to "kernelize" the features so that a linear SSVM framework can leverage the power of non-linear kernels without incurring the high computational cost. We demonstrate the advantages of this approach in a series of image segmentation experiments on the MSRC data set as well as 2D and 3D datasets containing imagery of neural tissue acquired with electron microscopes. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-33709-3_29 | ECCV (2) |
Keywords | Field | DocType |
state-of-the-art approach,image segmentation experiment,kernelized feature,powerful non-linear kernel,pairwise term,linear ssvm framework,non-linear kernel,linear kernel,conditional random fields,unary term,structured image segmentation,image segmentation,structured svm | Structured support vector machine,Conditional random field,Pairwise comparison,Feature vector,Scale-space segmentation,Pattern recognition,Computer science,Support vector machine,Structured prediction,Image segmentation,Artificial intelligence,Machine learning | Conference |
Volume | ISSN | Citations |
7573 | 0302-9743 | 25 |
PageRank | References | Authors |
0.82 | 26 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aurelien Lucchi | 1 | 2419 | 89.45 |
| Yunpeng Li | 2 | 578 | 45.91 |
| Kevin Smith | 3 | 2430 | 88.78 |
| Pascal Fua | 4 | 12768 | 731.45 |