Ales Leonardis
(University of Ljubljana, Slovenia)
The question how to represent visual information in an
artificial cognitive system to enable fast and reliable execution of
various cognitive tasks has been discussed throughout the history of
computer vision. The theories have converged towards hierarchical
architectures of parts composed of parts, (the so-called compositional
systems), starting with simple, frequent features that are gradually
combined into more and more complex entities. However, the automatic
design of parts in hierarchical layers has been hindered by a
theoretically enormous number of possible compositions. In this talk,
I will describe a novel approach that overcomes the exponential
complexity of unsupervised learning by exploiting the favorable
statistics of natural images in a sequential, hierarchical manner. The
parts recovered in the individual layers of the hierarchy vary from
simple to more complex ones and enable a fast indexing (bottom-up) and
matching (top-down) scheme that can be efficiently used for a variety
of cognitive tasks. I will show the results of the proposed approach
obtained on different data sets, yielding important insights for
designing compositional systems.