TV news retrieval based on story segmentation and concept association

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
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Communication dans un congrès
SITIS 2016 : 12th International Conference on Signal Image Technology & Internet Based Systems, Nov 2016, Naples, Italy. IEEE Computer Society, Proceedings SITIS 2016 : 12th International Conference on Signal Image Technology & Internet Based Systems, pp.327 - 334, 2016, 〈10.1109/SITIS.2016.60〉
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https://hal.archives-ouvertes.fr/hal-01520643
Contributeur : Médiathèque Télécom Sudparis & Institut Mines-Télécom Business School <>
Soumis le : mercredi 10 mai 2017 - 16:48:52
Dernière modification le : jeudi 31 mai 2018 - 09:12:02

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Ruxandra Tapu, Bogdan Mocanu, Titus Zaharia. TV news retrieval based on story segmentation and concept association. SITIS 2016 : 12th International Conference on Signal Image Technology & Internet Based Systems, Nov 2016, Naples, Italy. IEEE Computer Society, Proceedings SITIS 2016 : 12th International Conference on Signal Image Technology & Internet Based Systems, pp.327 - 334, 2016, 〈10.1109/SITIS.2016.60〉. 〈hal-01520643〉

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