Knowledge acquisition from databases
Knowledge acquisition from databases
MultiMediaMiner: a system prototype for multimedia data mining
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
Managing and Mining Multimedia Databases
Managing and Mining Multimedia Databases
"GeoPlot": spatial data mining on video libraries
Proceedings of the eleventh international conference on Information and knowledge management
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Video Data Mining: Semantic Indexing and Event Detection from the Association Perspective
IEEE Transactions on Knowledge and Data Engineering
Least significant bit steganography detection with machine learning techniques
Proceedings of the 2007 international workshop on Domain driven data mining
Efficient Algorithms for Video Association Mining
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
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
| Hi-index | 0.00 |
To support more efficient video database management, this paper explores the concept of video association mining, with which the association patterns are characterized by sequentially associated video shots and their cluster information. Given a continuous video sequence V, the video shot segmentation mechanism is first introduced to parse it into discrete shots. We then cluster shots into visually distinct groups and construct a shot cluster sequence by using the class label of each shot. An association mining scheme is designed to mine sequentially associated clusters from the sequence. Those detected associations will convey valuable knowledge for video database management. The experimental results demonstrate the effectiveness of our design.