Hierarchical clustering of WWW image search results using visual, textual and link information
Proceedings of the 12th annual ACM international conference on Multimedia
Web image clustering by consistent utilization of visual features and surrounding texts
Proceedings of the 13th annual ACM international conference on Multimedia
Pagerank for product image search
Proceedings of the 17th international conference on World Wide Web
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Smart batch tagging of photo albums
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Convergence Analysis of Affinity Propagation
KSEM '09 Proceedings of the 3rd International Conference on Knowledge Science, Engineering and Management
Visual query suggestion: Towards capturing user intent in internet image search
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Fast density-weighted low-rank approximation spectral clustering
Data Mining and Knowledge Discovery
Fast affinity propagation clustering: A multilevel approach
Pattern Recognition
MM '11 Proceedings of the 19th ACM international conference on Multimedia
What is a "Musical World"? An affinity propagation approach
MIRUM '11 Proceedings of the 1st international ACM workshop on Music information retrieval with user-centered and multimodal strategies
Mediapedia: mining web knowledge to construct multimedia encyclopedia
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Fast algorithm for affinity propagation
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Multimedia encyclopedia construction by mining web knowledge
Signal Processing
When Amazon Meets Google: Product Visualization by Exploring Multiple Web Sources
ACM Transactions on Internet Technology (TOIT)
Journal of Visual Communication and Image Representation
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In this paper, we propose a novel approach to organize image search results obtained from state-of-the-art image search engines in order to improve user experience. We aim to discover exemplars from search results and simultaneously group the images. The exemplars are delivered to the user as a summary of search results instead of the large amount of unorganized images. This gives the user a brief overview of search results with a small amount of images, and helps the user to further find the images of interest. We adopt the idea of affinity propagation and design a fast sparse affinity propagation algorithm to find exemplars that best represent the image search results. Experiments on real-world data demonstrate the effectiveness of our method both visually and quantitatively.