Video summarisation: A conceptual framework and survey of the state of the art

By Arthur G. Money, Harry W. Agius. Journal of Visual Communication and Image Representation, 2008

471 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Sociology and Political Science, Signal Processing

View Paper: https://www.semanticscholar.org/paper/414b7477daa7838b6bbd7af659683a965691272c

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Citations in the corpus

Visual to Text: Survey of Image and Video Captioning (2019, background): “Video summarization is usually converted to the problem of key frame extraction [31], where the appearance based features”
Multimodal Abstractive Summarization for How2 Videos (2019, background): “Video summarization is the task of producing a compact version of the video (visual summary) by encapsulating the most informative parts (Money and Agius, 2008; Lu and Grauman, 2013; Gygli et al., 2014; Song et al., 2015; Sah et al., 2017).”
First-person hyper-lapse videos (2014, background): “There has been a substantial amount of work on extracting important scenes from video [Chen et al. 2007; Detyniecki and Marsala 2008; Money and Agius 2008].”
Concept-Based Video Retrieval (2009, methodology): “Extensive overviews of methods for summarization are presented in [153] and [239].”
Towards Scalable Summarization of Consumer Videos Via Sparse Dictionary Selection (2012, background): “Interested readers can check [6] and [7] for a more comprehensive summary.”
Summarization of Egocentric Videos: A Comprehensive Survey (2017, background): “Whereas TPV segmentation approaches typically try to identify the shot boundaries comparing consecutive frames, or even using the frames’ time-stamp [33], [34], this methodology cannot be directly applied over FPV, since egocentric videos consist of a single shot with extremely smooth transitions be”
Video Summarization Using Deep Neural Networks: A Survey (2021, methodology): “[4] divide the bibliography into methods that rely on the analysis of the video stream, methods that process contextual video metadata, and hybrid approaches that rely on both types of the aforementioned data.”
Dissimilarity-based people re-identification and search for intelligent video surveillance (2013, background): “• summarisation of long footages to highlight only parts of potential interest [96].”
Predicting Important Objects for Egocentric Video Summarization (2015, methodology): “Both methods are modeled after standard keyframe and event detection methods [56,1,2].”