We propose a graph-based representation learning framework for video summarization.
First we convert an input video to a graph where nodes correspond to each of the video
frames. Then we impose sparsity on the graph by connecting only those pairs of nodes that
are within a specified temporal distance. We then formulate the video summarization task as
a binary node classification problem precisely classifying video frames whether they should
belong to the output summary video. A graph constructed this way aims to capture long …

We propose a graph-based representation learning framework for video summarization.
First, we convert an input video to a graph where nodes correspond to each of the video
frames. Then, we impose sparsity on the graph by connecting only those pairs of nodes that
are within a specified temporal distance. We then formulate the video summarization task as
a binary node classification problem, precisely classifying video frames whether they should
belong to the output summary video. A graph constructed this way aims to capture long …