Multimedia Tools and Applications

, Volume 62, Issue 2, pp 451–478 | Cite as

i-TagRanker: an efficient tag ranking system for image sharing and retrieval using the semantic relationships between tags



Folksonomy, considered a core component for Web 2.0 user-participation architecture, is a classification system made by user’s tags on the web resources. Recently, various approaches for image retrieval exploiting folksonomy have been proposed to improve the result of image search. However, the characteristics of the tags such as semantic ambiguity and non-controlledness limit the effectiveness of tags on image retrieval. Especially, tags associated with images in a random order do not provide any information about the relevance between a tag and an image. In this paper, we propose a novel image tag ranking system called i-TagRanker which exploits the semantic relationships between tags for re-ordering the tags according to the relevance with an image. The proposed system consists of two phases: 1) tag propagation phase, 2) tag ranking phase. In tag propagation phase, we first collect the most relevant tags from similar images, and then propagate them to an untagged image. In tag ranking phase, tags are ranked according to their semantic relevance to the image. From the experimental results on a Flickr photo collection about over 30,000 images, we show the effectiveness of the proposed system.


Tag ranking Tag-based image retrieval Semantic relationship Folksonomy WordNet 



This research was supported by the MKE(The Ministry of Knowledge Economy), Korea and Microsoft Research, under IT/SW Creative research program supervised by the NIPA(National IT Industry Promotion Agency) (NIPA-2010-C1810-1002-0012)


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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.KDE Laboratory, Department of Computer Science & EngineeringHanyang UniversityAnsan-siRepublic of Korea

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