[Lisa_seminaires] REMINDER: UdeM-McGill-MITACS machine learning seminar, PAA 1409 (UdeM), Fri Oct 19, 11:30am

Hugo Larochelle larocheh at iro.umontreal.ca
Ven 19 Oct 09:25:20 EDT 2007


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.



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