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MASSTagger: Metadata Aware Semantic Strategy for Automatic Image Tagging

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Digital Technologies and Applications (ICDTA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 454))

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Abstract

As the social media usage is expanding and people are using more and more platforms to share their stories through pictures, the use of image tags is becoming more relevant in the social media world. On social media. It is often seen that the option of tagging the images that are uploaded. Since image tagging is optional to the users, many images are posted untagged. Now the problem which arises is without image tags these images are hard to find. To overcome this issue many automatic tagging tools have been proposed. But image tagging requires precision and accuracy so that the images are tagged automatically based on the content in them. The proposed methodology uses Structural Topic Modelling and semantic similarity to tag the images and classify them into their respective categories.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, DIT University, Dehradun, India

    Shivam Sawarn & Gerard Deepak

  2. National Institute of Technology, Tiruchirappalli, India

    Gerard Deepak

Authors
  1. Shivam Sawarn
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  2. Gerard Deepak
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Corresponding author

Correspondence to Gerard Deepak .

Editor information

Editors and Affiliations

  1. Ecole Nationale des Sciences Appliquées, Fez, Morocco

    Saad Motahhir

  2. Faculty of Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Badre Bossoufi

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Sawarn, S., Deepak, G. (2022). MASSTagger: Metadata Aware Semantic Strategy for Automatic Image Tagging. In: Motahhir, S., Bossoufi, B. (eds) Digital Technologies and Applications. ICDTA 2022. Lecture Notes in Networks and Systems, vol 454. Springer, Cham. https://doi.org/10.1007/978-3-031-01942-5_43

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