A Caltech Library Service

Automatic discovery of image families: Global vs. local features

Aly, Mohamed and Welinder, Peter and Munich, Mario and Perona, Pietro (2009) Automatic discovery of image families: Global vs. local features. In: ICIP 2009 : 2009 IEEE International Conference on Image Processing. IEEE , Piscataway, NJ, pp. 777-780. ISBN 978-1-4244-5653-6.

PDF - Published Version
See Usage Policy.


Use this Persistent URL to link to this item:


Gathering a large collection of images has been made quite easy by social and image sharing websites, e.g. However, using such collections faces the problem that they contain a large number of duplicates and highly similar images. This work tackles the problem of how to automatically organize image collections into sets of similar images, called image families hereinafter. We thoroughly compare the performance of two approaches to measure image similarity: global descriptors vs. a set of local descriptors. We assess the performance of these approaches as the problem scales up to thousands of images and hundreds of families. We present our results on a new dataset of CD/DVD game covers.

Item Type:Book Section
Related URLs:
URLURL TypeDescription DOIArticle
Munich, Mario0000-0002-6665-7473
Perona, Pietro0000-0002-7583-5809
Additional Information:© 2009 IEEE.
Record Number:CaltechAUTHORS:20100823-091328531
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:19576
Deposited By: Tony Diaz
Deposited On:23 Aug 2010 20:58
Last Modified:03 Oct 2019 01:58

Repository Staff Only: item control page