Enhancing semantic and geographic annotation of web images via logistic canonical correlation regression

Liangliang Cao, Jie Yu, Jiebo Luo, Thomas S. Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Photo community sites such as Flickr and Picasa Web Album host a massive amount of personal photos with millions of new photos uploaded every month. These photos constitute an overwhelming source of images that require effective management. There is an increasingly imperative need for semantic annotation of these web images. This paper addresses the problem by considering two kinds of annotation: semantic annotation and geographic annotation. Both are useful for image search and retrieval and for facilitating communities and social networks. This paper proposes a novel method of Logistic Canonical Correlation Regression (LCCR) for the annotation task. This model exploits the canonical correlation between heterogeneous features and an annotation lexicon of interest, and builds a generalized annotation engine based on canonical correlations in order to produce enhanced annotation for web images. We validate the effectiveness of our algorithm using a dataset of over 380,000 images tagged with GPS coordinates.

Original languageEnglish (US)
Title of host publicationMM'09 - Proceedings of the 2009 ACM Multimedia Conference, with Co-located Workshops and Symposiums
Pages125-134
Number of pages10
DOIs
StatePublished - 2009
Externally publishedYes
Event17th ACM International Conference on Multimedia, MM'09, with Co-located Workshops and Symposiums - Beijing, China
Duration: Oct 19 2009Oct 24 2009

Publication series

NameMM'09 - Proceedings of the 2009 ACM Multimedia Conference, with Co-located Workshops and Symposiums

Other

Other17th ACM International Conference on Multimedia, MM'09, with Co-located Workshops and Symposiums
Country/TerritoryChina
CityBeijing
Period10/19/0910/24/09

Keywords

  • Canonical correlations
  • Geographic annotation
  • Web image annotation

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Software

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