Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Combining Textual and Visual Cues for Content-Based Image Retrieval on the World Wide Web
CBAIVL '98 Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries
Convex Optimization
Hierarchical clustering of WWW image search results using visual, textual and link information
Proceedings of the 12th annual ACM international conference on Multimedia
Grouping WWW Image Search Results by Novel Inhomogeneous Clustering Method
MMM '05 Proceedings of the 11th International Multimedia Modelling Conference
Web image clustering by consistent utilization of visual features and surrounding texts
Proceedings of the 13th annual ACM international conference on Multimedia
Isoperimetric Graph Partitioning for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Isoperimetric Partitioning: A New Algorithm for Graph Partitioning
SIAM Journal on Scientific Computing
Co-clustering Documents and Words Using Bipartite Isoperimetric Graph Partitioning
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Narrowing the semantic gap - improved text-based web document retrieval using visual features
IEEE Transactions on Multimedia
Image co-clustering with multi-modality features and user feedbacks
MM '09 Proceedings of the 17th ACM international conference on Multimedia
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Web image clustering has drawn significant attention in the research community recently. However, not much work has been done in using multi-modal information for clustering Web images. In this paper, we address the problem of Web image clustering by simultaneous integration of visual and textual features from a graph partitioning perspective. In particular, we modelled visual features, images, and words from the surrounding text of the images using a tripartite graph. This graph is actually considered as a fusion of two bipartite graphs that are partitioned simultaneously by the proposed Consistent Isoperimetric High-order Co-clustering(CIHC) framework. Although a similar approach has been adopted before, the main contribution of this work lies in the computational efficiency, quality in Web image clustering and scalability to large image repositories that CIHC is able to achieve. We demonstrate this through experimental results performed on real Web images.