By Topic

Towards automated large scale discovery of image families

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Aly, M. ; Comput. Vision Lab., Caltech, Pasadena, CA, USA ; Welinder, P. ; Munich, M. ; Perona, P.

Gathering large collections of images is quite easy nowadays with the advent of image sharing Web sites, such as However, such collections inevitably contain duplicates and highly similar images, what we refer to as image families. Automatic discovery and cataloguing of such similar images in large collections is important for many applications, e.g. image search, image collection visualization, and research purposes among others. In this work, we investigate this problem by thoroughly comparing two broad approaches for measuring image similarity: global vs. local features. We assess their performance as the image collection scales up to over 11,000 images with over 6,300 families. We present our results on three datasets with different statistics, including two new challenging datasets. Moreover, we present a new algorithm to automatically determine the number of families in the collection with promising results.

Published in:

Computer Vision and Pattern Recognition Workshops, 2009. CVPR Workshops 2009. IEEE Computer Society Conference on

Date of Conference:

20-25 June 2009