On contextual photo tag recommendation

Philip J. McParlane, Yashar Moshfeghi, Joemon M. Jose

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

7 Citations (Scopus)

Abstract

Image tagging is a growing application on social media websites, however, the performance of many auto-tagging methods are often poor. Recent work has exploited an image's context (e.g. time and location) in the tag recommendation process, where tags which co-occur highly within a given time interval or geographical area are promoted. These models, however, fail to address how and when different image contexts can be combined. In this paper, we propose a weighted tag recommendation model, building on an existing state-of-the-art, which varies the importance of time and location in the recommendation process, based on a given set of input tags. By retrieving more temporally and geographically relevant tags, we achieve statistically significant improvements to recommendation accuracy when testing on 519k images collected from Flickr. The result of this paper is an important step towards more effective image annotation and retrieval systems.
Original languageEnglish
Title of host publicationProceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval
Place of PublicationNew York, NY
Pages965-968
Number of pages4
DOIs
Publication statusPublished - 28 Jul 2013

Keywords

  • geolocation
  • photo tag recommendation
  • temporal
  • image tagging
  • social media

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