Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Machine Learning
Machine Learning
Semi-supervised Clustering by Seeding
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Clustering with Instance-level Constraints
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Retrieval of difficult image classes using svd-based relevance feedback
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Active learning with statistical models
Journal of Artificial Intelligence Research
Clustering by competitive agglomeration
Pattern Recognition
Adaptive prototype-based fuzzy classification
Fuzzy Sets and Systems
H-BayesClust: A New Hierarchical Clustering Based on Bayesian Networks
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Automatic Image Annotation with Relevance Feedback and Latent Semantic Analysis
Adaptive Multimedial Retrieval: Retrieval, User, and Semantics
Embedded lattices tree: An efficient indexing scheme for content based retrieval on image databases
Journal of Visual Communication and Image Representation
Adaptive active classification of cell assay images
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Efficient stochastic algorithms for document clustering
Information Sciences: an International Journal
Classification improvement of local feature vectors over the KNN algorithm
Multimedia Tools and Applications
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We consider data clustering problems where a limited amount of high-level semantic information, in the form of pairwise must-link and cannot-link constraints, can be acquired from the user. This form of supervision will guide the categorization of image databases in order to provide overviews that fit better user expectations. We propose here an effective semi-supervised clustering algorithm, Active Fuzzy Constrained Clustering (AFCC), that minimizes a competitive agglomeration-based cost function with fuzzy terms corresponding to pairwise constraints provided by the user. In order to minimize the amount of constraints required, we define an active mechanism for the selection of candidates for constraints. The comparisons performed on a simple benchmark and on a ground truth image database show that with AFCC the results of clustering can be significantly improved with few constraints, making this semi-supervised approach an attractive alternative in the categorization of image databases.