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On introducing timed tag-clouds in video lectures indexing

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Abstract

The amount of digital material in video lecture archives is growing rapidly, causing the search&retrieval process to be time-consuming and almost impractical. Indeed, after the search, students receive a list of videos and often must use VCR-like functions to find the specific piece of video that covers the searched topic. Therefore, a more efficient method for video retrieval in digital video lecture archives is needed. In this paper, we propose VLB (Video Lecture Browsing), a system designed to facilitate both the retrieval of video lectures within video archives and the finding of the most appropriate segment of a video lecture that covers a searched topic by automatically producing a general picture of the contents of a video lecture. To achieve these goals, the system introduces the idea of timed tag-clouds, which are produced with a combination of aural and visual analysis. Results of a MOS evaluation show that users highly appreciate the timed tag-clouds approach and a comparison study against other popular approaches shows that 93 % of users prefer to use VLB to handle video lectures.

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Notes

  1. The OCR generated lesson description is generated by “tesseract”, an open-source OCR engine.

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  1. Dipartimento di Comunicazione ed Economia, Università di Modena e Reggio Emilia, Reggio Emilia., Viale Allegri 9 -, 42121, Reggio Emilia, Italy

    Marco Furini

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  1. Marco Furini
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Correspondence to Marco Furini.

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Furini, M. On introducing timed tag-clouds in video lectures indexing. Multimed Tools Appl 77, 967–984 (2018). https://doi.org/10.1007/s11042-016-4282-5

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  • DOI: https://doi.org/10.1007/s11042-016-4282-5

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