Automated production of TV program trailer using electronic program guide

ABSTRACT

This paper proposes two automated methods for producing TV program trailers (short video clips to advertise the program). Program trailers are useful as the representative video of a content retrieval system that operates in a large archive of program videos. The two methods employ introductory descriptions from electronic program guides. The first method is based on the sentence similarity between the closed caption and the introductory text of the target program. We extract closed caption sentences that have the highest similarity for each introductory sentence, and then connect the corresponding video segments to make the representative video. A Bayesian belief network is used to calculate the similarity. The second method extracts several sentences that have the same textual features as those of a general introductory text, and determines the corresponding video sections. The features are learned by using the AdaBoost algorithm. These methods were used to generate trailers for actual TV programs, by which their effectiveness was verified.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL

General Terms:
Algorithms, Experimentation, Languages

Keywords:
AdaBoost, Bayesian belief network, TV program trailer, closed caption, electronic program guide