Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Segmentation Given Partial Grouping Constraints
IEEE Transactions on Pattern Analysis and Machine Intelligence
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Semi-supervised graph clustering: a kernel approach
ICML '05 Proceedings of the 22nd international conference on Machine learning
A tutorial on spectral clustering
Statistics and Computing
A Semi-supervised Learning Algorithm on Gaussian Mixture with Automatic Model Selection
Neural Processing Letters
Pairwise constraint propagation by semidefinite programming for semi-supervised classification
Proceedings of the 25th international conference on Machine learning
Star Shape Prior for Graph-Cut Image Segmentation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
From comparing clusterings to combining clusterings
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Gaussian mixture learning via robust competitive agglomeration
Pattern Recognition Letters
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Combining Context, Consistency, and Diversity Cues for Interactive Image Categorization
IEEE Transactions on Multimedia
Multi-modal constraint propagation for heterogeneous image clustering
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Modalities consensus for multi-modal constraint propagation
Proceedings of the 20th ACM international conference on Multimedia
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This paper presents an exhaustive and efficient constraint propagation approach to exploiting pairwise constraints for spectral clustering. Since traditional label propagation techniques cannot be readily generalized to propagate pairwise constraints, we tackle the constraint propagation problem inversely by decomposing it to a set of independent label propagation subproblems which are further solved in quadratic time using semi-supervised learning based on k-nearest neighbors graphs. Since this time complexity is proportional to the number of all possible pairwise constraints, our approach gives a computationally efficient solution for exhaustively propagating pairwise constraint throughout the entire dataset. The resulting exhaustive set of propagated pairwise constraints are then used to adjust the weight (or similarity) matrix for spectral clustering. It is worth noting that this paper first clearly shows how pairwise constraints are propagated independently and then accumulated into a conciliatory closed-form solution. Experimental results on real-life datasets demonstrate that our approach to constrained spectral clustering outperforms the state-of-the-art techniques.