Small codes and large image databases for recognition

Abstract

The Internet contains billions of images, freely available online. Methods for efficiently searching this incredibly rich resource are vital for a large number of applications. These include object recognition, computer graphics, personal photo collections, online image search tools. In this paper, our goal is to develop efficient image search and scene matching techniques that are not only fast, but also require very little memory, enabling their use on standard hardware or even on handheld devices. Our approach uses recently developed machine learning techniques to convert the Gist descriptor (a real valued vector that describes orientation energies at different scales and orientations within an image) to a compact binary code, with a few hundred bits per image. Using our scheme, it is possible to perform real-time searches with millions from the Internet using a single large PC and obtain recognition results comparable to the full descriptor. Using our codes on high quality labeled images from the LabelMe database gives surprisingly powerful recognition results using simple nearest neighbor techniques.

DOI: 10.1109/CVPR.2008.4587633

Extracted Key Phrases

9 Figures and Tables

05010020082009201020112012201320142015201620172018
Citations per Year

719 Citations

Semantic Scholar estimates that this publication has 719 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@article{Torralba2008SmallCA, title={Small codes and large image databases for recognition}, author={Antonio Torralba and Rob Fergus and Yair Weiss}, journal={2008 IEEE Conference on Computer Vision and Pattern Recognition}, year={2008}, pages={1-8} }