Events
The Taub Faculty of Computer Science Events and Talks
Yair Weiss (Hebrew University of Jerusalem)
Tuesday, 21.06.2011, 11:30
With the advent of the Internet it is now possible to collect hundreds
of millions of images for computer vision. These images come with
varying degrees of label information. "Clean" labels can be manually
obtained on a small fraction, "noisy labels" may be extracted
automatically from surrounding text, while for most images there are
no labels at all. Semi-supervised learning is a principled framework
for combining these different label sources. However, it scales
polynomially with the number of images, making it impractical for use
on gigantic collections with hundreds of millions of images and
thousands of classes. In this paper we show how to utilize recent
results in machine learning to obtain highly efficient approximations
for semi-supervised learning that are linear in the number of
images. Specifically, we use the convergence of the eigenvectors of
the normalized graph Laplacian to eigenfunctions of weighted
Laplace-Beltrami operators. Our algorithm enables us to apply
semi-supervised learning to a database of 80 million images gathered
from the Internet.