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VIRET at Video Browser Showdown 2020

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MultiMedia Modeling (MMM 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11962))

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Abstract

During the last three years, the most successful systems at the Video Browser Showdown employed effective retrieval models where raw video data are automatically preprocessed in advance to extract semantic or low-level features of selected frames or shots. This enables users to express their search intents in the form of keywords, sketch, query example, or their combination. In this paper, we present new extensions to our interactive video retrieval system VIRET that won Video Browser Showdown in 2018 and achieved the second place at Video Browser Showdown 2019 and Lifelog Search Challenge 2019. The new features of the system focus both on updates of retrieval models and interface modifications to help users with query specification by means of informative visualizations.

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Notes

  1. 1.

    The V3C1 dataset [18] is currently used at VBS.

  2. 2.

    Authors of a tool are considered to be experts as they are expected to use the tool more effectively.

  3. 3.

    https://cloud.google.com/speech-to-text/.

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Acknowledgments

This paper has been supported by Czech Science Foundation (GAČR) project 19-22071Y and by Charles University grant SVV-260451. We would also like to thank Přemysl Čech and Vít Škrhák for their help with interface in WPF.

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Correspondence to Jakub Lokoč .

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Lokoč, J., Kovalčík, G., Souček, T. (2020). VIRET at Video Browser Showdown 2020. In: Ro, Y., et al. MultiMedia Modeling. MMM 2020. Lecture Notes in Computer Science(), vol 11962. Springer, Cham. https://doi.org/10.1007/978-3-030-37734-2_70

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  • DOI: https://doi.org/10.1007/978-3-030-37734-2_70

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