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State-of-the-art and future challenges in video scene detection: a survey

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Abstract

In the last 15 years much effort has been made in the field of segmentation of videos into scenes. We give a comprehensive overview of the published approaches and classify them into seven groups based on three basic classes of low-level features used for the segmentation process: (1) visual-based, (2) audio-based, (3) text-based, (4) audio-visual-based, (5) visual-textual-based, (6) audio-textual-based and (7) hybrid approaches. We try to make video scene detection approaches better assessable and comparable by making a categorization of the evaluation strategies used. This includes size and type of the dataset used as well as the evaluation metrics. Furthermore, in order to let the reader make use of the survey, we list eight possible application scenarios, including an own section for interactive video scene segmentation, and identify those algorithms that can be applied to them. At the end, current challenges for scene segmentation algorithms are discussed. In the appendix the most important characteristics of the algorithms presented in this paper are summarized in table form.

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Notes

  1. http://mpeg.chiariglione.org/standards/mpeg-7/mpeg-7.htm (February 1, 2013).

  2. http://trecvid.nist.gov/ (February 1, 2013).

  3. http://www.beeldengeluid.nl/en (February 1, 2013).

  4. http://www.nist.gov/srd/nistsd26.cfm (February 1, 2013).

  5. If an approach has been evaluated with multiple video types, it is counted once for each corresponding genre. For the total number of approaches, such approaches are counted multiple times, once for each type of video. Therefore, the sum of all percentages in the chart in Fig. 12 is 100 %.

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Acknowledgments

Special thanks to Professor Alan Hanjalic from Delft University of Technology for his valuable thoughts and suggestions on how to structure this survey. This work was supported by Lakeside Labs GmbH, Klagenfurt, Austria and funding from the European Regional Development Fund and the Carinthian Economic Promotion Fund (KWF) under Grant KWF-20214 17097 24774 and Grant KWF-20214 22573 33955.

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  1. Institute of Information Technology (ITEC), Alpen-Adria-Universität Klagenfurt, Klagenfurt, Austria

    Manfred Del Fabro & Laszlo Böszörmenyi

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Correspondence to Manfred Del Fabro.

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Communicated by P. Pala.

Appendix

Appendix

See Tables 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 and 17.

Table 2 Overview of visual-based full segmentation
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Table 3 Overview of visual-based partial segmentation
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Table 4 Overview of visual graph-based full segmentation
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Table 5 Overview of visual stochastic-based full segmentation
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Table 6 Overview of audio-based full segmentation
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Table 7 Overview of audio-based partial segmentation
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Table 8 Overview of text-based full segmentation
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Table 9 Overview of audio-visual-based full segmentation
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Table 10 Overview of audio-visual graph-based full segmentation
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Table 11 Overview of audio-visual stochastic-based full segmentation
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Table 12 Overview of audio-visual stochastic-based partial segmentation
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Table 13 Overview of visual-textual-based full segmentation
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Table 14 Overview of audio-textual-based full segmentation
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Table 15 Overview of audio-textual-based partial segmentation
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Table 16 Overview of hybrid full segmentation
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Table 17 Overview of hybrid partial segmentation
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Del Fabro, M., Böszörmenyi, L. State-of-the-art and future challenges in video scene detection: a survey. Multimedia Systems 19, 427–454 (2013). https://doi.org/10.1007/s00530-013-0306-4

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