Beyond Shot Retrieval: Searching for Broadcast News Items Using Language Models of Concepts

  • Robin Aly
  • Aiden Doherty
  • Djoerd Hiemstra
  • Alan Smeaton
Conference paper

DOI: 10.1007/978-3-642-12275-0_23

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5993)
Cite this paper as:
Aly R., Doherty A., Hiemstra D., Smeaton A. (2010) Beyond Shot Retrieval: Searching for Broadcast News Items Using Language Models of Concepts. In: Gurrin C. et al. (eds) Advances in Information Retrieval. ECIR 2010. Lecture Notes in Computer Science, vol 5993. Springer, Berlin, Heidelberg

Abstract

Current video search systems commonly return video shots as results. We believe that users may better relate to longer, semantic video units and propose a retrieval framework for news story items, which consist of multiple shots. The framework is divided into two parts: (1) A concept based language model which ranks news items with known occurrences of semantic concepts by the probability that an important concept is produced from the concept distribution of the news item and (2) a probabilistic model of the uncertain presence, or risk, of these concepts. In this paper we use a method to evaluate the performance of story retrieval, based on the TRECVID shot-based retrieval groundtruth. Our experiments on the TRECVID 2005 collection show a significant performance improvement against four standard methods.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Robin Aly
    • 1
  • Aiden Doherty
    • 2
  • Djoerd Hiemstra
    • 1
  • Alan Smeaton
    • 2
  1. 1.Datbase SystemsUniversity TwenteEnschedeThe Netherlands
  2. 2.CLARITY: Center for Sensor Web TechnologyDublin City UniversityIreland

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