Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Web image clustering by consistent utilization of visual features and surrounding texts
Proceedings of the 13th annual ACM international conference on Multimedia
Bipartite isoperimetric graph partitioning for data co-clustering
Data Mining and Knowledge Discovery
Proceedings of the 17th international conference on World Wide Web
Dual-ranking for web image retrieval
Proceedings of the ACM International Conference on Image and Video Retrieval
Browsing an image database utilizing the associations between images and features
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
On clustering heterogeneous social media objects with outlier links
Proceedings of the fifth ACM international conference on Web search and data mining
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The visual appearance of an image is closely associated with its low-level features. Identifying the set of features that best characterizes the image is useful for tasks such as content-based image indexing and retrieval. In this paper, we present a method which simultaneously models and clusters large sets of images and their low-level visual features. A computational energy function suited for co-clustering images and their features is first constructed and a Hopfield model based stochastic algorithm is then developed for its optimization. We apply the method to cluster digital color photographs and present results to demonstrate its usefulness and effectiveness.