Algorithms for clustering data
Algorithms for clustering data
Trajectory clustering with mixtures of regression models
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian Approach to Temporal Data Clustering using Hidden Markov Models
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Using Dynamic Time Warping to Bootstrap HMM-Based Clustering of Time Series
Sequence Learning - Paradigms, Algorithms, and Applications
Learning and Recognizing Human Dynamics in Video Sequences
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Individual recognition from periodic activity using hidden Markov models
HUMO '00 Proceedings of the Workshop on Human Motion (HUMO'00)
Real-time American Sign Language recognition from video using hidden Markov models
ISCV '95 Proceedings of the International Symposium on Computer Vision
An information-theoretic analysis of hard and soft assignment methods for clustering
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
A novel technique for indexing video surveillance data
IWVS '03 First ACM SIGMM international workshop on Video surveillance
A PCA-based similarity measure for multivariate time series
Proceedings of the 2nd ACM international workshop on Multimedia databases
Clustering Time Series with Clipped Data
Machine Learning
Discovering Frequent Episodes and Learning Hidden Markov Models: A Formal Connection
IEEE Transactions on Knowledge and Data Engineering
Proceedings of the third ACM international workshop on Video surveillance & sensor networks
Integrating Hidden Markov Models and Spectral Analysis for Sensory Time Series Clustering
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Effective image and video mining: an overview of model-based approaches
MDM '05 Proceedings of the 6th international workshop on Multimedia data mining: mining integrated media and complex data
Unsupervised scene analysis: a hidden Markov model approach
Computer Vision and Image Understanding
Similarity-based analysis for large networks of ultra-low resolution sensors
Pattern Recognition
On-line trajectory clustering for anomalous events detection
Pattern Recognition Letters
On-line trajectory clustering for anomalous events detection
Pattern Recognition Letters - Special issue on vision for crime detection and prevention
An energy-based similarity measure for time series
EURASIP Journal on Advances in Signal Processing
Spectral Clustering and Embedding with Hidden Markov Models
ECML '07 Proceedings of the 18th European conference on Machine Learning
An Integrated Graph and Probability Based Clustering Framework for Sequential Data
DS '08 Proceedings of the 11th International Conference on Discovery Science
Detecting motion patterns via direction maps with application to surveillance
Computer Vision and Image Understanding
Intelligent Decision Technologies
Unsupervised scene analysis: A hidden Markov model approach
Computer Vision and Image Understanding
Movement Data Anonymity through Generalization
Transactions on Data Privacy
Clustering of vehicle trajectories
IEEE Transactions on Intelligent Transportation Systems
Machine learning approaches for time-series data based on self-organizing incremental neural network
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
State-space dynamics distance for clustering sequential data
Pattern Recognition
Spatial-temporal clustering of neural data using linked-mixtures of hidden Markov models
EURASIP Journal on Advances in Signal Processing - Special issue on statistical signal processing in neuroscience
Finding temporal patterns by data decomposition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Sequence classification via large margin hidden Markov models
Data Mining and Knowledge Discovery
Motion segmentation by model-based clustering of incomplete trajectories
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Motion trajectory clustering for video retrieval using spatio-temporal approximations
VISUAL'05 Proceedings of the 8th international conference on Visual Information and Information Systems
Integrating community matching and outlier detection for mining evolutionary community outliers
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
A novel framework for motion segmentation and tracking by clustering incomplete trajectories
Computer Vision and Image Understanding
Community trend outlier detection using soft temporal pattern mining
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Cross-Correlation Measure for Mining Spatio-Temporal Patterns
Journal of Database Management
Stock market co-movement assessment using a three-phase clustering method
Expert Systems with Applications: An International Journal
International Journal of Computer Vision
Unsupervised categorization of human motion sequences
Intelligent Data Analysis
Traffic event classification at intersections based on the severity of abnormality
Machine Vision and Applications
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A new approach is proposed for clustering time-series data. The approach can be used to discover groupings of similar object motions that were observed in a video collection. A finite mixture of hidden Markov models (HMMs) is fitted to the motion data using the expectation-maximization (EM) framework. Previous approaches for HMM-based clustering employ a k-means formulation, where each sequence is assigned to only a single HMM. In contrast, the formulation presented in this paper allows each sequence to belong to more than a single HMM with some probability, and the hard decision about the sequence class membership can be deferred until a later time when such a decision is required. Experiments with simulated data demonstrate the benefit of using this EM-based approach when there is more "overlap" in the processes generating the data. Experiments with real data show the promising potential of HMM-based motion clustering in a number of applications.