Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Video-based event recognition: activity representation and probabilistic recognition methods
Computer Vision and Image Understanding - Special issue on event detection in video
Fast Algorithms for Frequent Itemset Mining Using FP-Trees
IEEE Transactions on Knowledge and Data Engineering
Video Data Mining: Mining Semantic Patterns with temporal constraints from Movies
ISM '05 Proceedings of the Seventh IEEE International Symposium on Multimedia
Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Exploring temporal consistency for video analysis and retrieval
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Learning, detection and representation of multi-agent events in videos
Artificial Intelligence
Data mining using high performance data clouds: experimental studies using sector and sphere
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Extraction and temporal segmentation of multiple motion trajectories in human motion
Image and Vision Computing
Compute and storage clouds using wide area high performance networks
Future Generation Computer Systems
Video event segmentation and visualisation in non-linear subspace
Pattern Recognition Letters
An Improved Mixture Gaussian Models to Detect Moving Object Under Real-Time Complex Background
CW '08 Proceedings of the 2008 International Conference on Cyberworlds
A visual system for real time detection of goal events during soccer matches
Computer Vision and Image Understanding
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
A dynamic hierarchical clustering method for trajectory-based unusual video event detection
IEEE Transactions on Image Processing
A data placement strategy in scientific cloud workflows
Future Generation Computer Systems
Determining the best suited semantic events for cognitive surveillance
Expert Systems with Applications: An International Journal
Association and Temporal Rule Mining for Post-Filtering of Semantic Concept Detection in Video
IEEE Transactions on Multimedia
Trajectory-Based Anomalous Event Detection
IEEE Transactions on Circuits and Systems for Video Technology
Fusing audio vocabulary with visual features for pornographic video detection
Future Generation Computer Systems
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With the development of Internet technology, a vast number of video data are available. Mining the hidden relationship among semantic concepts in video is important for effective content-based video retrieval and has gained great attention recently. Cloud computing, as a cost-effective solution, has become popular in mining video data for storing the distributed data and computations. In this paper, we have developed a novel method based on frequent pattern tree (FPTree) for mining association rules in video retrieval. The core of the method is to extend the structure of FPTree by temporal parameter in motion events. Firstly, we get semantic concepts based trajectory retrieval, Ncuts has been used to classify the sub-events by trajectory segmentation, and the sub-events in each event have been annotated. Secondly, the new modeling, called temporal frequent pattern tree (TFPTree) is used to store motion event semantic concepts. And we propose the TFP-Growth algorithm to mine temporal frequent patterns from TFPTree for finding the rules of the motion events. As video datasets grow large, cloud-based infrastructure has been used to support our computing. The experiment shows our method is both efficient and effective in improving the accuracy of semantic concept detection in video retrieval.