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Towards data-driven estimation of image tag relevance using visually similar and dissimilar folksonomy images

Published: 29 October 2012 Publication History

Abstract

Given that the presence of non-relevant tags in an image folksonomy hampers the effective organization and retrieval of images, this paper discusses a novel technique for estimating the relevance of user-supplied tags with respect to the content of a seed image. Specifically, this paper proposes to compute the relevance of image tags by making use of both visually similar and dissimilar images. That way, compared to tag relevance estimation only using visually similar images, the difference in tag relevance between tags relevant and tags irrelevant with respect to the content of a seed image can be increased at a limited increase in computational cost, thus making it more straightforward to distinguish between them. The latter is confirmed through experimentation with subsets of MIRFLICKR-25000 and MIRFLICKR-1M, showing that tag relevance estimation using both visually similar and dissimilar images allows achieving more effective image tag refinement and tag-based image retrieval than tag relevance estimation only using visually similar images.

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  • (2023)Research in Collaborative Tagging Applications: Choosing the Right DatasetVAWKUM Transactions on Computer Sciences10.21015/vtcs.v11i1.130511:1(01-25)Online publication date: 5-Mar-2023
  • (2015)Quality-protected folksonomy maintenance approaches: a brief surveyThe Knowledge Engineering Review10.1017/S026988891500012030:05(521-544)Online publication date: 30-Oct-2015

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      cover image ACM Conferences
      SAM '12: Proceedings of the 2012 international workshop on Socially-aware multimedia
      October 2012
      68 pages
      ISBN:9781450315869
      DOI:10.1145/2390876
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      Published: 29 October 2012

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      Author Tags

      1. image folksonomies
      2. image retrieval
      3. image tag refinement
      4. socially-aware image understanding
      5. tag relevance estimation

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      MM '12: ACM Multimedia Conference
      October 29, 2012
      Nara, Japan

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      SAM '12 Paper Acceptance Rate 9 of 12 submissions, 75%;
      Overall Acceptance Rate 36 of 59 submissions, 61%

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      • (2023)Research in Collaborative Tagging Applications: Choosing the Right DatasetVAWKUM Transactions on Computer Sciences10.21015/vtcs.v11i1.130511:1(01-25)Online publication date: 5-Mar-2023
      • (2015)Quality-protected folksonomy maintenance approaches: a brief surveyThe Knowledge Engineering Review10.1017/S026988891500012030:05(521-544)Online publication date: 30-Oct-2015

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