SummaryNet: A Multi-Stage Deep Learning Model for Automatic Video Summarisation

• URL: http://arxiv.org/abs/2002.09424v1
• Date: Wed, 19 Feb 2020 18:24:35 GMT
• Title: SummaryNet: A Multi-Stage Deep Learning Model for Automatic Video Summarisation
• Authors: Ziyad Jappie and David Torpey and Turgay Celik
• Abstract summary: We introduce SummaryNet as a supervised learning framework for automated video summarisation. It employs a two-stream convolutional network to learn spatial (appearance) and temporal (motion) representations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video summarisation can be posed as the task of extracting important parts of a video in order to create an informative summary of what occurred in the video. In this paper we introduce SummaryNet as a supervised learning framework for automated video summarisation. SummaryNet employs a two-stream convolutional network to learn spatial (appearance) and temporal (motion) representations. It utilizes an encoder-decoder model to extract the most salient features from the learned video representations. Lastly, it uses a sigmoid regression network with bidirectional long short-term memory cells to predict the probability of a frame being a summary frame. Experimental results on benchmark datasets show that the proposed method achieves comparable or significantly better results than the state-of-the-art video summarisation methods.

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