RankCompete: Simultaneous ranking and clustering of web photos

Liangliang Cao, Andrey Del Pozo, Xin Jin, Jiebo Luo, Jiawei Han, Thomas S. Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution


With the explosive growth of digital cameras and online media, it has become crucial to design efficient methods that help users browse and search large image collections. The recent VisualRank algorithm [4] employs visual similarity to represent the link structure in a graph so that the classic PageRank algorithm can be applied to select the most relevant images. However, measuring visual similarity is difficult when there exist diversified semantics in the image collection, and the results from VisualRank cannot supply good visual summarization with diversity. This paper proposes to rank the images in a structural fashion, which aims to discover the diverse structure embedded in photo collections, and rank the images according to their similarity among local neighborhoods instead of across the entire photo collection. We design a novel algorithm named RankCompete, which generalizes the PageRank algorithm for the task of simultaneous ranking and clustering. The experimental results show that RankCompete outperforms VisualRank and provides an efficient but effective tool for organizing web photos.

Original languageEnglish (US)
Title of host publicationProceedings of the 19th International Conference on World Wide Web, WWW '10
Number of pages2
StatePublished - 2010
Event19th International World Wide Web Conference, WWW2010 - Raleigh, NC, United States
Duration: Apr 26 2010Apr 30 2010

Publication series

NameProceedings of the 19th International Conference on World Wide Web, WWW '10


Other19th International World Wide Web Conference, WWW2010
Country/TerritoryUnited States
CityRaleigh, NC


  • image ranking
  • image summarization
  • pagerank

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications


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