Comparing dissimilarity measures for content-based image retrieval

Haiming Liu, Dawei Song, Stefan Rüger, Rui Hu, Victoria Uren

Research output: Chapter in Book/Published conference outputConference publication


Dissimilarity measurement plays a crucial role in content-based image retrieval, where data objects and queries are represented as vectors in high-dimensional content feature spaces. Given the large number of dissimilarity measures that exist in many fields, a crucial research question arises: Is there a dependency, if yes, what is the dependency, of a dissimilarity measure’s retrieval performance, on different feature spaces? In this paper, we summarize fourteen core dissimilarity measures and classify them into three categories. A systematic performance comparison is carried out to test the effectiveness of these dissimilarity measures with six different feature spaces and some of their combinations on the Corel image collection. From our experimental results, we have drawn a number of observations and insights on dissimilarity measurement in content-based image retrieval, which will lay a foundation for developing more effective image search technologies.
Original languageEnglish
Title of host publicationInformation retrieval technology
Subtitle of host publication4th Asia Infomation Retrieval Symposium, AIRS 2008, Harbin, China, January 15-18, 2008 Revised Selected Papers
EditorsHang Li, Ting Liu, Wei-Ying Ma, Tetsuya Sakai, Kam-Fai Wong, Guodong Zhou
Place of PublicationBerlin (DE)
Number of pages7
Volume4993 LNCS
Publication statusPublished - 2008
Event4th Asia Infomation Retrieval Symposium - Harbin, China
Duration: 15 Jan 200818 Jan 2008

Publication series

NameLecture notes in computer science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference4th Asia Infomation Retrieval Symposium
Abbreviated titleAIRS 2008


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