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Linking segments of video using text-based methods and a flexible form of segmentation : How to index, query and re-rank data from the TRECVid (Blip.tv) dataset?

Wassenaar, J.D. (2018) Linking segments of video using text-based methods and a flexible form of segmentation : How to index, query and re-rank data from the TRECVid (Blip.tv) dataset?

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Abstract:In order to let user’s explore, and use large archives, videohyperlinking tries to aid the user in linking segments of video to other segments of videos, similar to the way hyperlinks on the web are used. Indexing, querying and re-ranking multimodal data, in this case video’s, are subjects common in the videohyperlinking community. A videohyperlinking system contains an index of multimodal (video) data, while the currently watched segment is translated into a query, the query generation phase. Finally, the system responds to the user with a ranked list of targets that are about the anchor segment. In this study, the payload of terms in the form of position and offset in Elastic Search are used to obtain time-based information along the speech transcripts to link users directly to spoken text. The queries are generated by a statistic-based method using TF-IDF, a grammar-based part-of-speech tagger or a combination of both. Finally, results are ranked by weighting specific components and cosine similarity. The system is evaluated with the Precision at 5 and MAiSP measures, which are used in the TRECVid benchmark on this topic. The results show that TF-IDF and the cosine similarity work the best for the proposed system.
Item Type:Essay (Master)
Clients:
Beeld en Geluid, Hilversum, The Netherlands
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Human Media Interaction MSc (60030)
Link to this item:http://purl.utwente.nl/essays/74848
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