Faceted metadata for image search and browsing
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Image Browsing using Hierarchical Clustering
ISCC '99 Proceedings of the The Fourth IEEE Symposium on Computers and Communications
Hierarchical clustering of WWW image search results using visual, textual and link information
Proceedings of the 12th annual ACM international conference on Multimedia
IGroup: presenting web image search results in semantic clusters
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Large-scale image annotation using visual synset
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
PRiSMA: searching images in parallel
Proceedings of the 20th ACM international conference on Multimedia
Image search—from thousands to billions in 20 years
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special Sections on the 20th Anniversary of ACM International Conference on Multimedia, Best Papers of ACM Multimedia 2012
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Web image retrieval systems, such as Google or Bing image search, present search results as a relevance-ordered list. Although alternative browsing models (e.g. results as clusters or hierarchies) have been proposed in the past, it remains to be seen whether such models can be applied to large-scale image search. This work presents Google Image Swirl, a large-scale, publicly available, hierarchical image browsing system by automatically group the search results based on visual and semantic similarity. This paper describes methods used to build such system and shares the findings from 2-years worth of user feedback and usage statistics.