by Johannes Wassenaar
Linking segments of video using text-based methods and a flexible form of segmentation
In order to let user’s explore, and use large archives, video hyperlinking 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 – instead of using a regular search tool. Indexing, querying and re-ranking multimodal data, in this case video’s, are subjects common in the video hyperlinking community. A video hyperlinking 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.
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