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Movie/Script: Alignment and Parsing of Video and Text Transcription

  • Timothee Cour
  • Chris Jordan
  • Eleni Miltsakaki
  • Ben Taskar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5305)

Abstract

Movies and TV are a rich source of diverse and complex video of people, objects, actions and locales “in the wild”. Harvesting automatically labeled sequences of actions from video would enable creation of large-scale and highly-varied datasets. To enable such collection, we focus on the task of recovering scene structure in movies and TV series for object tracking and action retrieval. We present a weakly supervised algorithm that uses the screenplay and closed captions to parse a movie into a hierarchy of shots and scenes. Scene boundaries in the movie are aligned with screenplay scene labels and shots are reordered into a sequence of long continuous tracks or threads which allow for more accurate tracking of people, actions and objects. Scene segmentation, alignment, and shot threading are formulated as inference in a unified generative model and a novel hierarchical dynamic programming algorithm that can handle alignment and jump-limited reorderings in linear time is presented. We present quantitative and qualitative results on movie alignment and parsing, and use the recovered structure to improve character naming and retrieval of common actions in several episodes of popular TV series.

Keywords

Travel Salesman Problem Hamiltonian Path Face Track Scene Segmentation Action Retrieval 
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.

Supplementary material

978-3-540-88693-8_12_MOESM1_ESM.mpg (9.8 mb)
Supplementary material(9,998 KB)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Timothee Cour
    • 1
  • Chris Jordan
    • 1
  • Eleni Miltsakaki
    • 1
  • Ben Taskar
    • 1
  1. 1.University of PennsylvaniaPhiladelphia, PAUSA

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