Learning the 2-D Topology of Images
| Type of publication: | Inproceedings |
| Citation: | NIPS2007-925 |
| Booktitle: | Advances in Neural Information Processing Systems 20 (NIPS'07) |
| Year: | 2008 |
| Pages: | 841--848 |
| Publisher: | MIT Press |
| Address: | Cambridge, MA |
| Crossref: | NIPS20: |
| Abstract: | We study the following question: is the two-dimensional structure of images a very strong prior or is it something that can be learned with a few examples of natural images? If someone gave us a learning task involving images for which the two-dimensional topology of pixels was not known, could we discover it automatically and exploit it? For example suppose that the pixels had been permuted in a fixed but unknown way, could we recover the relative two-dimensional location of pixels on images? The surprising result presented here is that not only the answer is yes but that about as few as a thousand images are enough to approximately recover the relative locations of about a thousand pixels. This is achieved using a manifold learning algorithm applied to pixels associated with a measure of distributional similarity between pixel intensities. We compare different topologyextraction approaches and show how having the two-dimensional topology can be exploited. |
| Keywords: | |
| Authors | |
| Editors | |
| Added by: | [ADM] |
| Total mark: | 0 |
|
Attachments
|
|
|
Notes
|
|
|
|
|
|
Topics
|
|
|
|
|
