Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Machine Learning
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
World-scale mining of objects and events from community photo collections
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Semantic video annotation by mining association patterns from visual and speech features
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Video mining with frequent itemset configurations
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
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In this paper, we present an approach to discover characteristic patterns in videos. We characterize the videos based on frequently occurring patterns like scenes, characters, sequence of frames in an unsupervised setting. With our approach, we are able to detect the representative scenes and characters of movies. We also present a method for detecting video stop-words in broadcast news videos based on the frequency of occurrence of sequence of frames. These are analogous to stop-words in text classification and search. We employ two different video mining schemes; both aimed at detecting frequent and representative patterns. For one of our mining approaches, we use an efficient frequent pattern mining algorithm over a quantized feature space. Our second approach uses a Random Forest to first represent video data as sequences, and then mine the frequent patterns. We validate the proposed approaches on broadcast news videos and our database of 81 Oscar winning movies.