Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published May 2004 | Accepted Version
Book Section - Chapter Open

A visual category filter for Google images

Abstract

We extend the constellation model to include heterogeneous parts which may represent either the appearance or the geometry of a region of the object. The pans and their spatial configuration are learnt simultaneously and automatically, without supervision, from cluttered images. We describe how this model can be employed for ranking the output of an image search engine when searching for object categories. It is shown that visual consistencies in the output images can be identified, and then used to rank the images according to their closeness to the visual object category. Although the proportion of good images may be small, the algorithm is designed to be robust and is capable of learning in either a totally unsupervised manner, or with a very limited amount of supervision. We demonstrate the method on image sets returned by Google's image search for a number of object categories including bottles, camels, cars, horses, tigers and zebras.

Additional Information

© 2004 Springer. Financial support was provided by: EC Project CogViSys; UK EPSRC; Caltech CNSE and the NSF.

Attached Files

Accepted Version - Fergus_ECCV4.pdf

Files

Fergus_ECCV4.pdf
Files (3.7 MB)
Name Size Download all
md5:e45817e5a5949326d92044ae0b12abaf
3.7 MB Preview Download

Additional details

Created:
August 19, 2023
Modified:
January 13, 2024