Google image swirl: a large-scale content-based image visualization system

Yushi Jing, Henry A. Rowley, Jingbin Wang, David Tsai, Chuck Rosenberg, Michele Covell

Abstract: 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.