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Using Object Detection, NLP, and Knowledge Bases to Understand the Message of Images

  • Lydia Weiland
  • Ioana Hulpus
  • Simone Paolo Ponzetto
  • Laura Dietz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10133)

Abstract

With the increasing amount of multimodal content from social media posts and news articles, there has been an intensified effort towards conceptual labeling and multimodal (topic) modeling of images and of their affiliated texts. Nonetheless, the problem of identifying and automatically naming the core abstract message (gist) behind images has received less attention. This problem is especially relevant for the semantic indexing and subsequent retrieval of images. In this paper, we propose a solution that makes use of external knowledge bases such as Wikipedia and DBpedia. Its aim is to leverage complex semantic associations between the image objects and the textual caption in order to uncover the intended gist. The results of our evaluation prove the ability of our proposed approach to detect gist with a best MAP score of 0.74 when assessed against human annotations. Furthermore, an automatic image tagging and caption generation API is compared to manually set image and caption signals. We show and discuss the difficulty to find the correct gist especially for abstract, non-depictable gists as well as the impact of different types of signals on gist detection quality.

Keywords

Mean Average Precision Seed Node Wildlife Corridor Manual Caption Original Caption 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work is funded by the RiSC programme of the Ministry of Science, Research and the Arts Baden-Wuerttemberg, and used computational resources offered from the bwUni-Cluster within the framework program bwHPC. Furthermore, this work was in part funded through the Elitepostdoc program of the BW-Stiftung and the University of New Hampshire.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Lydia Weiland
    • 1
  • Ioana Hulpus
    • 1
  • Simone Paolo Ponzetto
    • 1
  • Laura Dietz
    • 2
  1. 1.University of MannheimMannheimGermany
  2. 2.University of New HampshireDurhamUSA

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