Image retrieval using short binary codes

The obvious way to find images that are semantically similar to a query image is to solve the object recognition problem. In the meantime, it is possible to extract a feature vector from each image and to retrieve images with similar features. If the features are binary they are cheap to store and match. If they are also highly abstract (e.g. indoor vs outdoor) and roughly orthogonal they work well for image retrieval. I will describe a method of extracting such binary features using multilayer neural networks. I will then show how binary codes can be used retrieve a shortlist of semantically similar images extremely rapidly in a time that is independent of the size of the database. This is work in progress with Alex Krizhevsky.

Speaker Details

Geoff Hinton is a fellow of the Canadian Institute for Advanced Research and Professor of Department of Computer Science at the University of Toronto. He spent three years from 1998 until 2001 setting up the Gatsby Computational Neuroscience Unit at University College London and then returned to the University of Toronto where he is a University Professor. He is the director of the program on “Neural Computation and Adaptive Perception” which is funded by the Canadian Institute for Advanced Research. Geoffrey Hinton is a fellow of the Royal Society and an honorary foreign member of the American Academy of Arts and Sciences. He was one of the researchers who introduced the back-propagation algorithm that has been widely used for practical applications. His other contributions to neural network research include Boltzmann machines, distributed representations, time-delay neural nets, mixtures of experts, Helmholtz machines and products of experts. More recently, he and his collaborators have introduced efficient, unsupervised, learning procedures for deep networks containing many layers of non-linear features. They have demonstrated that, in addition to creating excellent generative models, this unsupervised learning greatly improves classification performance in a variety of domains.

Date:
Speakers:
Geoff Hinton
Affiliation:
University of Toronto