Sum and Difference Histograms for Texture Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Inference in model-based cluster analysis
Statistics and Computing
Finding overlapping components with MML
Statistics and Computing
Novel mixtures based on the dirichlet distribution: application to data and image classification
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Incremental learning of retrieval knowledge in a case-based reasoning system
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
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
Acquisition of concept descriptions by conceptual clustering
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
IEEE Transactions on Image Processing
Editorial: Recent advances in data mining
Engineering Applications of Artificial Intelligence
Positive vectors clustering using inverted Dirichlet finite mixture models
Expert Systems with Applications: An International Journal
A variational statistical framework for object detection
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Spatial color image segmentation based on finite non-Gaussian mixture models
Expert Systems with Applications: An International Journal
Infinite Dirichlet mixture models learning via expectation propagation
Advances in Data Analysis and Classification
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This paper presents an online algorithm for mixture model-based clustering. Mixture modeling is the problem of identifying and modeling components in a given set of data. The online algorithm is based on unsupervised learning of finite Dirichlet mixtures and a stochastic approach for estimates updating. For the selection of the number of clusters, we use the minimum message length (MML) approach. The proposed method is validated by synthetic data and by an application concerning the dynamic summarization of image databases.