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Semi-Supervised Learning in Gigantic Image Collections
Rob Fergus · Yair Weiss · Antonio Torralba

Tue Dec 08 07:00 PM -- 11:59 PM (PST) @ None #None

With the advent of the Internet it is now possible to collect hundreds of millions of images. 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. We combine this with a label sharing framework obtained from Wordnet to propagate label information to classes lacking manual annotations. Our algorithm enables us to apply semi-supervised learning to a database of 80 million images with 74 thousand classes.

Author Information

Rob Fergus (DeepMind / NYU)

Rob Fergus is an Associate Professor of Computer Science at the Courant Institute of Mathematical Sciences, New York University. He received a Masters in Electrical Engineering with Prof. Pietro Perona at Caltech, before completing a PhD with Prof. Andrew Zisserman at the University of Oxford in 2005. Before coming to NYU, he spent two years as a post-doc in the Computer Science and Artificial Intelligence Lab (CSAIL) at MIT, working with Prof. William Freeman. He has received several awards including a CVPR best paper prize, a Sloan Fellowship & NSF Career award and the IEEE Longuet-Higgins prize.

Yair Weiss (Hebrew University)

Yair Weiss is an Associate Professor at the Hebrew University School of Computer Science and Engineering. He received his Ph.D. from MIT working with Ted Adelson on motion analysis and did postdoctoral work at UC Berkeley. Since 2005 he has been a fellow of the Canadian Institute for Advanced Research. With his students and colleagues he has co-authored award winning papers in NIPS (2002),ECCV (2006), UAI (2008) and CVPR (2009).

Antonio Torralba (Massachusetts Institute of Technology)

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