document: RankCompete : simultaneous ranking and clustering of web photos 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 can not 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 .
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