Time and Location:   April 7, 2004, 2-3:00pm, Room 1314.

Title: The Intrinsic Geometry of Natural Image Data and Learning by Diffusion

Speaker:  Ann Lee, Yale University

Abstract:

An important question in vision research is how to best model and code
natural images. We will first explore the empirical statistics of large
sets of patches from images in a normal visual environment and ask what is
the intrinsic geometry of natural image data. A new picture of natural
image statistics seems to emerge from this study, where geometric
primitives (such as edges, blobs and bars) generate a complex hierarchy of
non-linear manifolds where the data is densely concentrated in state
space. In the second part of the talk, we will then describe a general
methodology for organizing high-dimensional data sets by embedding them in
Euclidean space via a non-linear diffusion map.  Preliminary results will
be shown for both generic image patches and hyperspectral data of
pathology slices.