Algorithms for clustering data
Algorithms for clustering data
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
ACM Computing Surveys (CSUR)
Evaluation of hierarchical clustering algorithms for document datasets
Proceedings of the eleventh international conference on Information and knowledge management
Alternatives to the k-means algorithm that find better clusterings
Proceedings of the eleventh international conference on Information and knowledge management
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
Semi-supervised Clustering by Seeding
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
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
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Integrating constraints and metric learning in semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
K-means clustering via principal component analysis
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Locality preserving clustering for image database
Proceedings of the 12th annual ACM international conference on Multimedia
Learning a Mahalanobis Metric from Equivalence Constraints
The Journal of Machine Learning Research
Toward Robust Distance Metric Analysis for Similarity Estimation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Learning Distance Metrics with Contextual Constraints for Image Retrieval
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Intractability and clustering with constraints
Proceedings of the 24th international conference on Machine learning
Local and Global Structures Preserving Projection
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 02
Image retrieval in multipoint queries
International Journal of Imaging Systems and Technology - Multimedia Information Retrieval
Identifying and generating easy sets of constraints for clustering
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
CLUE: cluster-based retrieval of images by unsupervised learning
IEEE Transactions on Image Processing
Unsupervised image-set clustering using an information theoretic framework
IEEE Transactions on Image Processing
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Constrained locally weighted clustering
Proceedings of the VLDB Endowment
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Image clustering is useful in many retrieval and classification applications. The main goal of image clustering is to partition a given dataset into salient clusters such that the images in each cluster appear visually similar to each other compared with those in other clusters. In this paper, we propose a semi-supervised clustering algorithm, which leverages the accumulated user query log to guide the clustering process. Guided by the log file, our method arranges images into small groups and constructs a graph that captures the dissimilar relations between the groups. Each group is assigned to a feasible cluster. Our analysis reveals that the probability of image points being assigned to the correct clusters is much higher by our new proposal, compared to conventional methods. Our algorithm can produce image clusters close to the ground truth and satisfying the semantic relations between the images inferred from the query log. Experimental results further confirm the superiority of our design.