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A video hard cut detection using multifractal features

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Abstract

Efficient management of video sequences is based on adequate video content description. This description can be used for various purposes in different applications, telecommunication services, video and multimedia systems. Video hard cut detection represents the foundation of temporal video segmentation. In this paper, a new video hard cut detection methodology is proposed using multifractal features. Transition between two shots can be described as color and texture differences within a decoded video sequence. In the proposed methodology we formed specific structures by measuring color differences between frames. The formed structures are used for hard cut candidate detection. This is followed by multifractal representation of texture changes by Hölder exponents. The proposed methodology achieves high performance using more than 750,000 frames, extracted from forty different video sequences, classified by four well known genre groups. Moreover, the proposed hard cut detection achieves high performance regardless of high level video production or complex non-linear editing for different genre groups. This is confirmed by comparison between the proposed methodology and other recent work on hard cut detection.

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Authors and Affiliations

  1. ICT College of Vocational Studies, Zdravka Celara 16, Belgrade, Serbia

    Goran Zajic

  2. School of Electrical Engineering, University of Belgrade, Bulevar Kralja Aleksandra 73, Belgrade, Serbia

    Goran Zajic, Ana Gavrovska, Irini Reljin & Branimir Reljin

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  1. Goran Zajic
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Correspondence to Goran Zajic.

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Zajic, G., Gavrovska, A., Reljin, I. et al. A video hard cut detection using multifractal features. Multimed Tools Appl 78, 6233–6252 (2019). https://doi.org/10.1007/s11042-018-6420-8

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