Exploiting Multimodality in Video Hyperlinking to Improve Target Diversity

• Main Authors: Bois, Rémi, Vukotić, Vedran, Simon, Anca-Roxana, Sicre, Ronan, Raymond, Christian, Sébillot, Pascale, Gravier, Guillaume
• Other Authors: Creating and exploiting explicit links between multimedia fragments (LinkMedia), MEDIA ET INTERACTIONS (IRISA-D6), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT)-Université de Rennes 1 (UR1), Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria), LABEX CominLabs Linking Media in Acceptable Hypergraphs, Amsaleg, Laurent, Guðmundsson, Gylfi Þór, Gurrin, Cathal, Satoh, Shin’ichi
• Format: Conference Object
• Language: English
• Published: HAL CCSD 2017
• Subjects: [INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM]; [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing; [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing; [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; Iceland
• Online Access: https://hal.archives-ouvertes.fr/hal-01498130; https://hal.archives-ouvertes.fr/hal-01498130v2/document; https://hal.archives-ouvertes.fr/hal-01498130v2/file/diversity.pdf; https://doi.org/10.1007/978-3-319-51814-5_16

International audience Video hyperlinking is the process of creating links within a collection of videos to help navigation and information seeking. Starting from a given set of video segments, called anchors, a set of related segments, called targets, must be provided. In past years, a number of content-based approaches have been proposed with good results obtained by searching for target segments that are very similar to the anchor in terms of content and information. Unfortunately, relevance has been obtained to the expense of diversity. In this paper, we study multimodal approaches and their ability to provide a set of diverse yet relevant targets. We compare two recently introduced cross-modal approaches, namely, deep auto-encoders and bimodal LDA, and experimentally show that both provide significantly more diverse targets than a state-of-the-art baseline. Bimodal autoencoders offer the best trade-off between relevance and diversity, with bimodal LDA exhibiting slightly more diverse targets at a lower precision.