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Key frame extraction scheme based on sliding window and features

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Abstract

With the rapid development of the Internet and P2P technology, multimedia resources are gradually adding and used widely. Since network traffic increases sharply, how to choose the interested information for a number of Internet users is challenging. So, technologies and applications, such as video search, video fast browsing, video index and storage are in great demand. Behind these technologies and applications, an important problem is how to quickly browse massive video data and obtain the main content of the video. To solve this problem, different key frame extraction algorithms have been proposed. Due to the diversity of video content, different video have different characteristics. So the design of general video key frame extraction algorithm to solve the problem is not the reality. The main trend for the problem is to design the key frame extraction algorithm based on the characteristics of the video itself. In this article, we mainly focus on videos with edited boundaries and shot conversions. Aiming at this kind of video, we have designed and implemented video key frame extraction algorithm based on sliding window, the global feature Gist and local feature point detection algorithm SURF. In this algorithm, we use Gist feature to construct the global scene information of frames,and the SURF key point detection algorithm to extract local key points as local feature for each frame. Then, shot segmentation based on sliding window and shot merging algorithm is applied to dividing the original video into several shots. After that,we select the most representative frames in each video shot as key frames. Finally we evaluate the result of the algorithm from the subjective and objective perspective. Results show that key frames extracted in the algorithm are of high quality and can basically cover the main content of the original video.

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Acknowledgment

This work is supported by the National Natural Science Foundation of China (No. 61502439).

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Authors and Affiliations

  1. School of Computer, China University of Geosciences(Wuhan), Wuhan, 430074, China

    Linchen Yu, Jigang Cao, Mianlong Chen & Xinyue Cui

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  1. Linchen Yu
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  2. Jigang Cao
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  3. Mianlong Chen
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  4. Xinyue Cui
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Corresponding author

Correspondence to Linchen Yu.

Additional information

This article is part of the Topical Collection: Special Issue on Big Data Networking

Guest Editors: Xiaofei Liao, Song Guo, Deze Zeng, and Kun Wang

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Yu, L., Cao, J., Chen, M. et al. Key frame extraction scheme based on sliding window and features. Peer-to-Peer Netw. Appl. 11, 1141–1152 (2018). https://doi.org/10.1007/s12083-017-0567-3

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  • DOI: https://doi.org/10.1007/s12083-017-0567-3

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