TV News Retrieval Based on Story Segmentation and Concept Association

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper we propose a novel method for TV news retrieval. A first stage concerns a temporal segmentation into stories units. Then, for each story the most relevant concepts are extracted based on a multimodal fusion between visual and textual information. By analyzing the video stream, we perform global frame representation, image retrieval and re-ranking, in order to determine, with high confidence, the segments boundaries. In addition, by using the video subtitle, we identify the most relevant concepts/topics addressed in each independent segment. The framework is evaluated using one week video archive of France Television and 20 journals from NBC and CNN TV stations. For the temporal video segmentation, our system returns high precision and recall scores, superior to 90%. Regarding the topic association technique, we obtain a mean average precision score superior to 0.5.

Original languageEnglish
Title of host publicationProceedings - 12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016
EditorsGiuseppe De Pietro, Albert Dipanda, Richard Chbeir, Luigi Gallo, Kokou Yetongnon
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages327-334
Number of pages8
ISBN (Electronic)9781509056989
DOIs
Publication statusPublished - 21 Apr 2017
Externally publishedYes
Event12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016 - Naples, Italy
Duration: 28 Nov 20161 Dec 2016

Publication series

NameProceedings - 12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016

Conference

Conference12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016
Country/TerritoryItaly
CityNaples
Period28/11/161/12/16

Keywords

  • TV news segmentation
  • concept association
  • story units
  • visual and textual information

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