Name
Abstract | ||
|---|---|---|
Advances in object detection have made it possible to collect large databases of certain objects. In this paper we exploit these datasets for within-object classification. For example, we classify gender in face images, pose in pedestrian images and phenotype in cell images. Previous work has mainly targeted the above tasks individually using object specific representations. Here, we propose a general Bayesian framework for within-object classification. Images are represented as a regular grid of non-overlapping patches. In training, these patches are approximated by a predefined library. In inference, the choice of approximating patch determines the classification decision. We propose a Bayesian framework in which we marginalize over the patch frequency parameters to provide a posterior probability for the class. We test our algorithm on several challenging “real world” databases. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICCV.2009.5459352 | Kyoto |
Keywords | DocType | Volume |
Bayes methods,image classification,image representation,maximum likelihood estimation,object detection,visual databases,a posterior probability,cell images,face images,general Bayesian framework,large databases,non-overlapping patches,object classification,object detection,object specific representations,pedestrian images | Conference | 2009 |
Issue | ISSN | ISBN |
1 | 1550-5499 E-ISBN : 978-1-4244-4419-9 | 978-1-4244-4419-9 |
Citations | PageRank | References |
13 | 0.61 | 14 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jania Aghajanian | 1 | 42 | 2.98 |
| Jonathan Warrell | 2 | 494 | 18.95 |
| Simon Prince | 3 | 914 | 60.61 |
| Peng Li | 4 | 126 | 6.21 |
| Jennifer L. Rohn | 5 | 13 | 0.61 |
| Buzz Baum | 6 | 31 | 3.35 |