Designing video data management systems
Designing video data management systems
Event detection from time series data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Video Manga: generating semantically meaningful video summaries
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
A fast string searching algorithm
Communications of the ACM
An Information Theoretic Approach to Rule Induction from Databases
IEEE Transactions on Knowledge and Data Engineering
VideoCube: A Novel Tool for Video Mining and Classification
ICADL '02 Proceedings of the 5th International Conference on Asian Digital Libraries: Digital Libraries: People, Knowledge, and Technology
Fast Multi-dimensional Approximate Pattern Matching
CPM '99 Proceedings of the 10th Annual Symposium on Combinatorial Pattern Matching
Discovery of Core Episodes from Sequences
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
An Unsupervised Algorithm for Segmenting Categorical Timeseries into Episodes
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Bursty and hierarchical structure in streams
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Associating characters with events in films
Proceedings of the 6th ACM international conference on Image and video retrieval
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‘Video data mining’ is a technique to discover useful patterns from videos. It plays an important role in efficient video management. Particularly, we concentrate on extracting useful editing patterns from movies. These editing patterns are useful for an amateur editor to produce a new, more attractive video. But, it is essential to extract editing patterns associated with their semantic contents, called ‘semantic structures’. Otherwise the amateur editor can’t determine how to use the extracted editing patterns during the process of editing a new video. In this paper, we propose two approaches to extract semantic structures from a movie, based on two different time series models of the movie. In one approach, the movie is represented as a multi-stream of metadata derived from visual and audio features in each shot. In another approach, the movie is represented as one-dimensional time series consisting of durations of target character’s appearance and disappearance. To both time series models, we apply data mining techniques. As a result, we extract the semantic structures about shot transitions and about how the target character appears on the screen and disappears from the screen.