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Graph-Based Label Propagation in Digital Media: A Review

Published: 01 April 2015 Publication History

Abstract

The expansion of the Internet over the last decade and the proliferation of online social communities, such as Facebook, Google+, and Twitter, as well as multimedia sharing sites, such as YouTube, Flickr, and Picasa, has led to a vast increase of available information to the user. In the case of multimedia data, such as images and videos, fast querying and processing of the available information requires the annotation of the multimedia data with semantic descriptors, that is, labels. However, only a small proportion of the available data are labeled. The rest should undergo an annotation-labeling process. The necessity for the creation of automatic annotation algorithms gave birth to label propagation and semi-supervised learning. In this study, basic concepts in graph-based label propagation methods are discussed. Methods for proper graph construction based on the structure of the available data and label inference methods for spreading label information from a few labeled data to a larger set of unlabeled data are reviewed. Applications of label propagation algorithms in digital media, as well as evaluation metrics for measuring their performance, are presented.

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cover image ACM Computing Surveys
ACM Computing Surveys  Volume 47, Issue 3
April 2015
602 pages
ISSN:0360-0300
EISSN:1557-7341
DOI:10.1145/2737799
  • Editor:
  • Sartaj Sahni
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 01 April 2015
Accepted: 01 December 2014
Revised: 01 July 2014
Received: 01 January 2014
Published in CSUR Volume 47, Issue 3

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  1. Semi-supervised learning
  2. label propagation

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