Large-scale multimedia content analysis using scientific workflows

  • Authors:

  • Ricky J. Sethi;Yolanda Gil;Hyunjoon Jo;Andrew Philpot

  • Affiliations:

  • University of Southern California, Marina del Rey, CA, USA;University of Southern California, Marina del Rey, CA, USA;University of Southern California, Marina del Rey, CA, USA;University of Southern California, Marina del Rey, CA, USA

  • Venue:
  • Proceedings of the 21st ACM international conference on Multimedia
  • Year:
  • 2013

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Abstract

Analyzing web content, particularly multimedia content, for security applications is of great interest. However, it often requires deep expertise in data analytics that is not always accessible to non-experts. Our approach is to use scientific workflows that capture expert-level methods to examine web content. We use workflows to analyze the image and text components of multimedia web posts separately, as well as by a multimodal fusion of both image and text data. In particular, we re-purpose workflow fragments to do the multimedia analysis and create additional components for the fusion of the image and text modalities. In this paper, we present preliminary work which focuses on a Human Trafficking Detection task to help deter human trafficking of minors by thus fusing image and text content from the web. We also examine how workflow fragments save time and effort in multimedia content analysis while bringing together multiple areas of machine learning and computer vision. We further export these workflow fragments using linked data as web objects.