This paper describes the system we designed to address the Hyperlinking task at TRECVID 2017 and the achieved results. Our contribution explores the potential of a solution based on the combination of textual and visual features in order to consider the different facets of the input videos. In particular, our approaches combined automatically generated transcripts (LIMSI), visual concepts, Meta-data, the text extracted by means of a Name Entity Recognition technique and a concept mapping tool. The four submitted runs aimed at analyzing the impact of the considered features on the quality of the retrieved hyperlinks.

Eurecom-Polito at TRECVID 2017: Hyperlinking task / Benoit, Huet; Baralis, E.; Garza, P.; Kavoosifar, MOHAMMAD REZA. - ELETTRONICO. - (2017). ((Intervento presentato al convegno 2017 TRECVID Workshop tenutosi a Gaithersburg, Maryland USA nel November 13-15, 2017.

Eurecom-Polito at TRECVID 2017: Hyperlinking task

E. Baralis;P. Garza;KAVOOSIFAR, MOHAMMAD REZA
2017

Abstract

This paper describes the system we designed to address the Hyperlinking task at TRECVID 2017 and the achieved results. Our contribution explores the potential of a solution based on the combination of textual and visual features in order to consider the different facets of the input videos. In particular, our approaches combined automatically generated transcripts (LIMSI), visual concepts, Meta-data, the text extracted by means of a Name Entity Recognition technique and a concept mapping tool. The four submitted runs aimed at analyzing the impact of the considered features on the quality of the retrieved hyperlinks.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2713297
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