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Identifying Tags Describing Image Contents

Authors:

Taka Maenishi,

Keishi TajimaAuthors Info & Claims

HT '19: Proceedings of the 30th ACM Conference on Hypertext and Social Media

Pages 297 - 298

https://doi.org/10.1145/3342220.3344936

Published: 12 September 2019 Publication History

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Abstract

On many photo-sharing social media, e.g., Instagram, a user posting a photo can add tags, i.e., words describing it. Tags are used for keyword-based image search. Some tags, however, describe not image contents but some metadata, e.g., camera names. We propose a method of determining which tag is more likely to describe the associated image contents. We determine it based on pairwise comparisons of tags. Given a pair of tags A and B, we compare three sets of images: (1) those associated with A, (2) those associated with B, and (3) those associated with both A and B. If (3) is more similar to (1) than to (2), A is more likely to describe image contents. Our experiment with Instagram data shows that our method is effective.

References

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Cited By

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Zhang YYamakata YTajima K(2021)MIRecipe: A Recipe Dataset for Stage-Aware Recognition of Changes in Appearance of IngredientsProceedings of the 3rd ACM International Conference on Multimedia in Asia10.1145/3469877.3490596(1-7)Online publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1145/3469877.3490596
Zhang YYamakata YTajima KMougiakakou SMaria Farinella GYanai K(2019)Categorization of Cooking Actions Based on Textual/Visual SimilarityProceedings of the 5th International Workshop on Multimedia Assisted Dietary Management10.1145/3347448.3357165(42-49)Online publication date: 15-Oct-2019
https://dl.acm.org/doi/10.1145/3347448.3357165

Index Terms

Identifying Tags Describing Image Contents
1. Information systems
  1. Information retrieval
    1. Specialized information retrieval
      1. Multimedia and multimodal retrieval
        Image search
  2. World Wide Web
    1. Web searching and information discovery
      1. Social tagging

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Information & Contributors

Information

Published In

HT '19: Proceedings of the 30th ACM Conference on Hypertext and Social Media

September 2019

326 pages

ISBN:9781450368858

DOI:10.1145/3342220

General Chairs:
Claus Atzenbeck
Hof University, Germany
,
Jessica Rubart
OWL University of Applied Sciences and Arts, Germany
,
Program Chair:
David E. Millard
University of Southampton, UK

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 September 2019

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Japan Science and Technology Agency

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HT '19

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HT '19: 30th ACM Conference on Hypertext and Social Media

September 17 - 20, 2019

Hof, Germany

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HT '19 Paper Acceptance Rate 20 of 68 submissions, 29%;

Overall Acceptance Rate 378 of 1,158 submissions, 33%

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Cited By

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Zhang YYamakata YTajima K(2021)MIRecipe: A Recipe Dataset for Stage-Aware Recognition of Changes in Appearance of IngredientsProceedings of the 3rd ACM International Conference on Multimedia in Asia10.1145/3469877.3490596(1-7)Online publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1145/3469877.3490596
Zhang YYamakata YTajima KMougiakakou SMaria Farinella GYanai K(2019)Categorization of Cooking Actions Based on Textual/Visual SimilarityProceedings of the 5th International Workshop on Multimedia Assisted Dietary Management10.1145/3347448.3357165(42-49)Online publication date: 15-Oct-2019
https://dl.acm.org/doi/10.1145/3347448.3357165

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Concept-Level Multimodal Ranking of Flickr Photo Tags via Recall Based Weighting

Social image tag enrichment based on textual similarity modeling

Social tags: meaning and suggestions