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Automatic detection of slide transitions in lecture videos

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Abstract

This paper presents a method to automatically detect slide changes in lecture videos. For accurate detection, the regions capturing slide images are first identified from video frames. Then, SIFT features are extracted from the regions, which are invariant to image scaling and rotation. These features are used to compare similarity between frames. If the similarity is smaller than a threshold, slide transition is detected. The threshold is estimated based on the mean and standard deviation of sample frames’ similarities. Using this method, high detection accuracy can be obtained without any supplementary slide images. The proposed method also supports detection of backward slide transitions that occur when a speaker returns to a previous slide to emphasize its contents. In experiments conducted on our test collection, the proposed method showed 87 % accuracy in forward transition detection and 86 % accuracy in backward transition detection.

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Notes

  1. 1 We only consider f i,j satisfying 1≤i < j≤k, because f i,j is equal to f j,i , and f i,i is meaningless.

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Acknowledgments

This research was supported by the MSIP(Ministry of Science, ICT and Future Planning) of Korea under the ITRC support program(NIPA-2013-H0301-13-4009), and the National Research Foundation of Korea grant funded by the Korea government(MEST) (No. 2012R1A2A2A01046694).

This paper was supported by the Sahmyook University Research Fund in 2013.

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Authors and Affiliations

  1. Department of Computer Science, KAIST, 373-1 Guseong-Dong, Yuseong-Gu, Daejeon, 305-701, Republic of Korea

    Hyun Ji Jeong, Tak-Eun Kim & Myoung Ho Kim

  2. Department of Computer Engineering, Sahmyook University, Hwarangro 815, Nowon-Gu, Seoul, 139-742, Republic of Korea

    Hyeon Gyu Kim

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  1. Hyun Ji Jeong
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  2. Tak-Eun Kim
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  3. Hyeon Gyu Kim
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  4. Myoung Ho Kim
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Correspondence to Hyeon Gyu Kim.

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Jeong, H.J., Kim, TE., Kim, H.G. et al. Automatic detection of slide transitions in lecture videos. Multimed Tools Appl 74, 7537–7554 (2015). https://doi.org/10.1007/s11042-014-1990-6

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  • DOI: https://doi.org/10.1007/s11042-014-1990-6

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