Automatic discovery of image families: Global vs. local features

Gathering a large collection of images has been made quite easy by social and image sharing websites, e.g. flickr.com. 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 org...

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Main Authors: Aly, M., Welinder, P., Munich, M., Perona, P.
Format: Conference Proceeding
Language:English
Subjects:
Clustering algorithms
Computer vision
Face detection
Image retrieval
Internet
Object recognition
Partitioning algorithms
Robot vision systems
Robotics and automation
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Welinder, P.
Munich, M.
Perona, P.
description Gathering a large collection of images has been made quite easy by social and image sharing websites, e.g. flickr.com. 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.
doi_str_mv 10.1109/ICIP.2009.5414235
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subjects Clustering algorithms
Computer vision
Face detection
Image retrieval
Internet
Object recognition
Partitioning algorithms
Robot vision systems
Robotics and automation
title Automatic discovery of image families: Global vs. local features
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