ACM Computing Surveys (CSUR)
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Distributed clustering using collective principal component analysis
Knowledge and Information Systems
Finding Consistent Clusters in Data Partitions
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Scene-Centered Description from Spatial Envelope Properties
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Semantic Organization of Scenes Using Discriminant Structural Templates
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Clustering Algorithms and Validity Measures
SSDBM '01 Proceedings of the 13th International Conference on Scientific and Statistical Database Management
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
A clustering method based on boosting
Pattern Recognition Letters
Subspace clustering for high dimensional data: a review
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
K-means clustering via principal component analysis
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Hierarchical clustering of WWW image search results using visual, textual and link information
Proceedings of the 12th annual ACM international conference on Multimedia
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
New Routes from Minimal Approximation Error to Principal Components
Neural Processing Letters
Two-way Gaussian mixture models for high dimensional classification
Statistical Analysis and Data Mining
Boosting GMM and its two applications
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
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Gaussian Mixture Model (GMM) is widely used in unsupervised learning tasks. In this paper, we propose the boost-GMM algorithm which uses GMMs to cluster real world scenes. At first, images will be extracted with gist-feature to get the data set. At each boosting iteration, a new training set is constructed by using weighted sampling from the original dataset and GMM is used to provide a new data partitioning. The final clustering solution is produced by aggregating the multiple clustering results. Experiments on real-world scene sets indicate that boost-GMM has higher result than other algorithms.