Automatic appropriate segment extraction from shots based on learning from example videos

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Abstract

Videos are composed of shots, each of which is recorded continuously by a camera, and video editing can be considered as a process of re-sequencing shots selected from original videos. Shots usually include redundant intervals, which are often edited out by professional editors. Defining the intact interval which is used in the edited video as the appropriate segment and all other intervals of equal length as inappropriate segments, this paper proposes a method for automatically extracting appropriate segments from shots. Since what kinds of characteristics make an interval appropriate to be used in the edited video should be different among shots with different content, the proposed method firstly categorizes shots according to their content with Support Vector Machines. Then, the appropriate segments are extracted based on the temporal patterns of audio and visual features in appropriate and inappropriate segments learned with Hidden Markov Models for each shot category. The effectiveness of the proposed method is verified with experiments. © 2009 Springer Berlin Heidelberg.

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APA

Kurihara, Y., Nitta, N., & Babaguchi, N. (2009). Automatic appropriate segment extraction from shots based on learning from example videos. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5414 LNCS, pp. 1082–1093). https://doi.org/10.1007/978-3-540-92957-4_94

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