Learning to cluster web search results
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Hierarchical clustering of WWW image search results using visual, textual and link information
Proceedings of the 12th annual ACM international conference on Multimedia
IGroup: web image search results clustering
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Clustering with Bregman Divergences
The Journal of Machine Learning Research
Designing Novel Image Search Interfaces by Understanding Unique Characteristics and Usage
INTERACT '09 Proceedings of the 12th IFIP TC 13 International Conference on Human-Computer Interaction: Part II
Linking video ads with product or service information by web search
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Web image retrieval via learning semantics of query image
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Multimodal image collection visualization using non-negative matrix factorization
ECDL'10 Proceedings of the 14th European conference on Research and advanced technology for digital libraries
Using ephemeral clustering and query logs to organize web image search results on mobile devices
IMMPD '11 Proceedings of the 2011 international ACM workshop on Interactive multimedia on mobile and portable devices
Extended CBIR via learning semantics of query image
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Multimedia search reranking: A literature survey
ACM Computing Surveys (CSUR)
Journal of Visual Communication and Image Representation
Clustering results of image searches by annotations and visual features
Telematics and Informatics
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Usually, the image search results contain multiple topics on semantic level and even semantically consistent images have diverse appearances on visual level. How to organize the results into semantically and visually consistent clusters becomes a necessary task to facilitate users' navigation. To attack this, HiCluster, an effective method to organize image search results is designed in this paper, which employs both textual and visual analysis. First, we extract some query-related key phrases to enumerate specific semantics of the given query and cluster them into some semantic clusters using K-lines-based clustering algorithm. Second, the resulting images corresponding to each key phrase are clustered with Bregman Bubble Clustering (BBC) algorithm, which partially groups images in the whole set while discarding some scattered noisy ones. At last, a novel user interface (UI) is designed to provide users with the diverse and helpful information based on the hierarchical clustering structure. Experiments on web images demonstrate the effectiveness and potential of the system.