Lessons for the future from a decade of informedia video analysis research

  • Authors:

  • Alexander G. Hauptmann

  • Affiliations:

  • School of Computer Science, Carnegie Mellon University, Pittsburgh, PA

  • Venue:
  • CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
  • Year:
  • 2005

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Abstract

The overarching goal of the Informedia Digital Video Library project has been to achieve machine understanding of video media, including all aspects of search, retrieval, visualization and summarization in both contemporaneous and archival content collections. The base technology developed by the Informedia project combines speech, image and natural language understanding to automatically transcribe, segment and index broadcast video for intelligent search and image retrieval. While speech processing has been the most influential component in the success of the Informedia project, other modalities can be critical in various situations. Evaluations done in the context of the TRECVID benchmarks show that while some progress has been made, there is still a lot of work ahead. The fundamental “semantic gap” still exists, but there are a number of promising approaches to bridging it.