This week's seminar (see http://www.iro.umontreal.ca/article.php3?id_article=107&lang=en):
Learning the 2-D Topology of Images
by Pascal Lamblin, Département d’Informatique et Recherche Opérationnelle Université de Montréal
Location: Pavillon André-Aisenstadt (UdeM), room 1409 Time: October 19th 2007, 11h30
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 topology-extraction approaches and show how having the two-dimensional topology can be exploited.