Learning task-specific object recognition and scene understanding
Computer Vision and Image Understanding
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Network Intrusion Detection: An Analyst's Handbook
Network Intrusion Detection: An Analyst's Handbook
Analysis and Results of the 1999 DARPA Off-Line Intrusion Detection Evaluation
RAID '00 Proceedings of the Third International Workshop on Recent Advances in Intrusion Detection
Automatic Video Summarization by Graph Modeling
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Enhanced Model-Based Clustering, Density Estimation,and Discriminant Analysis Software: MCLUST
Journal of Classification
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters - Special issue: Artificial neural networks in pattern recognition
A Visual Vocabulary for Flower Classification
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Unsupervised Selection of a Finite Dirichlet Mixture Model: An MML-Based Approach
IEEE Transactions on Knowledge and Data Engineering
Video abstraction: A systematic review and classification
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
On fast supervised learning for normal mixture models with missing information
Pattern Recognition
A new intrusion detection system using support vector machines and hierarchical clustering
The VLDB Journal — The International Journal on Very Large Data Bases
Online clustering via finite mixtures of Dirichlet and minimum message length
Engineering Applications of Artificial Intelligence
A Dirichlet process mixture of generalized Dirichlet distributions for proportional data modeling
IEEE Transactions on Neural Networks
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Expectation-propagation for the generative aspect model
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Expectation propagation for approximate Bayesian inference
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Infinite Liouville mixture models with application to text and texture categorization
Pattern Recognition Letters
On fitting finite dirichlet mixture using ECM and MML
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
MML-Based approach for finite dirichlet mixture estimation and selection
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
AdaBoost-Based Algorithm for Network Intrusion Detection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Probabilistic techniques for intrusion detection based on computer audit data
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Content analysis of video using principal components
IEEE Transactions on Circuits and Systems for Video Technology
A novel video key-frame-extraction algorithm based on perceived motion energy model
IEEE Transactions on Circuits and Systems for Video Technology
Hi-index | 0.00 |
In this article, we propose a novel Bayesian nonparametric clustering algorithm based on a Dirichlet process mixture of Dirichlet distributions which have been shown to be very flexible for modeling proportional data. The idea is to let the number of mixture components increases as new data to cluster arrive in such a manner that the model selection problem (i.e. determination of the number of clusters) can be answered without recourse to classic selection criteria. Thus, the proposed model can be considered as an infinite Dirichlet mixture model. An expectation propagation inference framework is developed to learn this model by obtaining a full posterior distribution on its parameters. Within this learning framework, the model complexity and all the involved parameters are evaluated simultaneously. To show the practical relevance and efficiency of our model, we perform a detailed analysis using extensive simulations based on both synthetic and real data. In particular, real data are generated from three challenging applications namely images categorization, anomaly intrusion detection and videos summarization.