Elements of information theory
Elements of information theory
Parametric Distributional Clustering for Image Segmentation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Hierarchical browsing and search of large image databases
IEEE Transactions on Image Processing
Annealing and the normalized N-cut
Pattern Recognition
Detecting image spam using visual features and near duplicate detection
Proceedings of the 17th international conference on World Wide Web
An information theoretic approach to speaker diarization of meeting data
IEEE Transactions on Audio, Speech, and Language Processing
The multi-view information bottleneck clustering
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Cov-HGMEM: an improved hierarchical clustering algorithm
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
Some question to Monte-Carlo simulation in AIB algorithm
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
Image disorder characterization based on rate distortion
CAEPIA'05 Proceedings of the 11th Spanish association conference on Current Topics in Artificial Intelligence
Finding the optimal cardinality value for information bottleneck method
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
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A new method for unsupervised image category clustering is presented, based on a continuous version of a recently introduced information theoretic principle, the information bottleneck (IB). The clustering method is based on hierarchical grouping: Utilizing a Gaussian mixture model, each image in a given archive is first represented as a set of coherent regions in a selected feature space. Images are next grouped such that the mutual information between the clusters and the image content is maximally preserved. The appropriate number of clusters can be determined directly from the IB principle. Experimental results demonstrate the performance of the proposed clustering method on a real image database.