Network video summarization based on key frame extraction via superpixel segmentation,Transactions on Emerging Telecommunications Technologies

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Network video summarization based on key frame extraction via superpixel segmentation
Transactions on Emerging Telecommunications Technologies ( IF 2.5 ) Pub Date : 2020-03-23 , DOI: 10.1002/ett.3940
Haiyan Jin _{1,

2} , Yang Yu ₁ , Yumeng Li ₁ , Zhaolin Xiao _{1,

2}

Affiliation

The spread of insecure online video has been a serious social problem. The video summarization becomes one of key step for automatic filtering the expected video from the Internet. At present, the most existing video summarization methods are based on calculating the image similarity between video frames, so that the key frame can be selected properly. In this article, we introduce a superpixel segmentation based image similarity calculation, and then the metric is applied into video summarization. To identify the video key frames, we introduce superpixel segmentation to cluster the pixels locally by estimating the optical flow displacement field between successive frames, which can extract key frames and reduce video redundancy. On the VSUMM dataset and YouTube dataset, the experimental results demonstrate that the proposed method has clear advantages on both subjectively qualitative analysis and objectively quantitative evaluation comparing with the state of the art methods.

中文翻译：

基于超像素分割关键帧提取的网络视频摘要

不安全的在线视频的传播已经成为一个严重的社会问题。视频摘要成为自动过滤来自互联网的预期视频的关键步骤之一。目前，大多数现有的视频摘要方法都是基于计算视频帧之间的图像相似度，以便正确选择关键帧。在本文中，我们介绍了一种基于超像素分割的图像相似度计算，然后将该度量应用于视频摘要。为了识别视频关键帧，我们引入了超像素分割，通过估计连续帧之间的光流位移场来局部聚类像素，这可以提取关键帧并减少视频冗余。在 VSUMM 数据集和 YouTube 数据集上，

更新日期：2020-03-23

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