Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Web image clustering by consistent utilization of visual features and surrounding texts
Proceedings of the 13th annual ACM international conference on Multimedia
Image clustering with tensor representation
Proceedings of the 13th annual ACM international conference on Multimedia
Spectral clustering for multi-type relational data
ICML '06 Proceedings of the 23rd international conference on Machine learning
Maximum unfolded embedding: formulation, solution, and application for image clustering
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Deriving semantics for image clustering from accumulated user feedbacks
Proceedings of the 15th international conference on Multimedia
Clustering web images with multi-modal features
Proceedings of the 15th international conference on Multimedia
Non-negative matrix factorization for semi-supervised data clustering
Knowledge and Information Systems
On context-aware co-clustering with metadata support
Journal of Intelligent Information Systems
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In Content-based Image Retrieval (CBIR) research, advanced technology that fuses the heterogeneous information into image clustering has drawn extensive attention recently. However, using multiple features for co-clustering images without any user feedbacks is a challenging problem. In this paper, we propose a Semi-Supervised Non-negative Matrix Factorization (SS-NMF) framework for image co-clustering. Our method computes new relational matrices by incorporating user provided feedbacks into images through simultaneous distance metric learning and feature selection for different low-level visual features. Using an iterative algorithm, we perform tri-factorizations of the new matrices to infer image clusters. Theoretically, we show the convergence and correctness of SS-NMF co-clustering and the advantages of SS-NMF co-clustering over existing approaches. Through extensive experiments conducted on image data sets, we demonstrate that SS-NMF provides an effective and efficient solution for image co-clustering.