Hierarchical clustering of WWW image search results using visual, textual and link information
Proceedings of the 12th annual ACM international conference on Multimedia
Measuring performance of web image context extraction
Proceedings of the Tenth International Workshop on Multimedia Data Mining
Multi-modal constraint propagation for heterogeneous image clustering
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Bilinear deep learning for image classification
MM '11 Proceedings of the 19th ACM international conference on Multimedia
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Clustering of images from search results can improve the user experience of image search. Most of the existing systems use both visual features and surrounding texts as signals for clustering while this paper demonstrates the use of an external knowledge base to make better sense out of the text signals in a prototype system called CISC. Once we understand the semantics of the text better, the result of the clustering is significantly improved. In addition to clustering the images by their semantic entities, our system can also conceptualize each image cluster into a set of concepts to represent the meaning of the cluster.