A Formal Study of Shot Boundary Detection

By Jinhui Yuan, Huiyi Wang, Lan Xiao, Wujie Zheng, Jianmin Li, Fuzong Lin, Bo Zhang. IEEE transactions on circuits and systems for video technology (Print), 2007

383 citations recorded by the source corpus.

This paper conducts a formal study of the shot boundary detection problem. First, a general formal framework of shot boundary detection techniques is proposed. Three critical techniques, i.e., the representation of visual content, the construction of continuity signal and the classification of continuity values, are identified and formulated in the perspective of pattern recognition. Meanwhile, the major challenges to the framework are identified. Second, a comprehensive review of the existing approaches is conducted. The representative approaches are categorized and compared according to their roles in the formal framework. Based on the comparison of the existing approaches, optimal criteria for each module of the framework are discussed, which will provide practical guide for developing novel methods. Third, with all the above issues considered, we present a unified shot boundary detection system based on graph partition model. Extensive experiments are carried out on the platform of TRECVID. The experiments not only verify the optimal criteria discussed above, but also show that the proposed approach is among the best in the evaluation of TRECVID 2005. Finally, we conclude the paper and present some further discussions on what shot boundary detection can learn from other related fields

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/5e82e0d082982b9a15bec47b468ae7c3fbcb5227

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In Computer Vision and Pattern Recognition

Citations in the corpus

Semantic Pooling for Complex Event Analysis in Untrimmed Videos (2016, methodology): “Other segmentation or change-point detection algorithms, such as [51], may also be used.”
A Survey on Visual Content-Based Video Indexing and Retrieval (2011, methodology): “The research on shot boundary detection has a long history, and there exist specific surveys on video shot boundary detection [16], [263].”
Concept-Based Video Retrieval (2009, background): “The automatic segmentation of video into shots has a long history and is by now a well understood problem [23] [297] for which highly robust automatic methods exist [292].”
Focus: Querying Large Video Datasets with Low Latency and Low Cost (2018, background): “Among them, most works focus on indexing videos for different types of queries such as shot boundary detection [78], semantic video search [30], video classiﬁcation [25], or spatio-temporal information-based video retrieval [61].”
Unsupervised Perceptual Rewards for Imitation Learning (2016, methodology): “segmentation methods [35, 17] which would likely enable a less constrained set of demonstrations to be used.”
Computing (2021, background): “Techniques that can evaluate human emotion associated with interactions of the built environment can inform how we design these environments both architec turally and through policy.”