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International Conference on Multimedia Modeling

MMM 2012: Advances in Multimedia Modeling pp 1Cite as

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A New Gap to Bridge: Where to Go Next in Social Media Retrieval?

A New Gap to Bridge: Where to Go Next in Social Media Retrieval?

(Extended Abstract)

  • Alan Hanjalic22 
  • Conference paper
  • 2020 Accesses

  • 2 Citations

Part of the Lecture Notes in Computer Science book series (LNISA,volume 7131)

Abstract

Research in Multimedia Information Retrieval (MIR) aims at matching multimedia content and user needs and so at bringing image, audio and video content together with users. Users expectations regarding multimedia content access in terms of semantically rich and personalized relevance criteria have always been high and have imposed high demands on the level of sophistication of MIR solutions. The potential to develop MIR technology that meets such high demands has rapidly grown over the past twenty years by building on intensive international research effort. This growth accelerated, however, with the increasing contextualization of images, video and music in rapidly expanding social networks that link distributed content, diverse metadata and users of various profiles and interests. It is clear that user demands regarding the sophistication of MIR technology have further grown in the social network context in view of new ways of interacting with multimedia content and with other people via and about this content. However, this new context has also brought vast new opportunities for improving the quality of MIR solutions. These opportunities lie in synergetic integrations of multidisciplinary scientific contributions and rich information resources found there. Revisiting MIR from the viewpoint of the social network context, using the approaches that are often jointly referred to as social media retrieval, can help the field not only resolve the problems that impeded its development in the past, but also address the new emerging demands. I will show how contextualizing the MIR in online networked communities of users can help us achieve a fundamental shift in the MIR grand challenge, from bridging the research-oriented semantic gap to bridging the much more important, user-oriented utility gap, that explicitly addresses the overall usefulness of a MIR system output for the user. I will highlight some of the opportunities in pursuing this new, utility-oriented MIR grand challenge.

Keywords

  • Social Network
  • Computer Vision
  • High Demand
  • International Research
  • Extend Abstract

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.

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Author information

Authors and Affiliations

  1. Delft Multimedia Information Retrieval Lab, Delft University of Technology, Mekelweg 4, 2628 CD, Delft, The Netherlands

    Alan Hanjalic

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  1. Alan Hanjalic
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Editor information

Editors and Affiliations

  1. Institute of Information Technology, Alpen-Adria-Universität Klagenfurt, Universitätsstr. 65-67, 9020, Klagenfurt, Austria

    Klaus Schoeffmann

  2. EURECOM, 2229 Rout des Crêtes, BP 193, 06904, Sophia Antipolis Cedex, France

    Bernard Merialdo

  3. School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave, 15213-3890, Pittsburgh, PA, USA

    Alexander G. Hauptmann

  4. Department of Computer Science, City University of Hong Kong, Tat Chee Ave, Kowloon, Hong Kong

    Chong-Wah Ngo

  5. Department of Electronic and Electrical Engineering, University College London, Roberts Building, Torrington Place, WC1E 7JE, London, UK

    Yiannis Andreopoulos

  6. Institute of Software Technology and Interactive Systems, Vienna University of Technology, Favoritenstrasse 9-11 188/2, 1040, Vienna, Austria

    Christian Breiteneder

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© 2012 Springer-Verlag Berlin Heidelberg

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Hanjalic, A. (2012). A New Gap to Bridge: Where to Go Next in Social Media Retrieval?. In: Schoeffmann, K., Merialdo, B., Hauptmann, A.G., Ngo, CW., Andreopoulos, Y., Breiteneder, C. (eds) Advances in Multimedia Modeling. MMM 2012. Lecture Notes in Computer Science, vol 7131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27355-1_1

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  • DOI: https://doi.org/10.1007/978-3-642-27355-1_1

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  • Print ISBN: 978-3-642-27354-4

  • Online ISBN: 978-3-642-27355-1

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