Content Based Video Summarization: Finding Interesting Temporal Sequences of Frames
Madhav Datt, Jayanta Mukhopadhyay · 2018
We present a novel video summarization model to generate coherent video summaries capturing the most interesting parts of a video, using CNN and bidirectional LSTMs to generate deep features for frame representation and to model variable-range temporal sequences. Further, we introduce a parameterized loss function minimizing KL-divergence between GMMs to learn relative orders of frame importances. We conduct extensive evaluation on several benchmarks (TV-Sum, SumMe and YouTube) to demonstrate the effectiveness of our model, where our approach significantly outperforms state-of-the-art methods in several settings. Given the enormous growth in user-generated videos, video summarization has increasing importance in being able to navigate, browse, and search videos efficiently. Our research could see direct applications in tackling problems like detecting break - ins from surveillance videos, generating sporting event highlights, etc.