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Title: Comparing dissimilarity measures for content-based image retrieval.
Authors: Liu, Haiming
Song, Dawei
Ruger, Stefan
Hu, Rui
Uren, Victoria
Editors: Li, Hang
Liu, Ting
Ma, Wei-Ying
Sakai, Tetsuya
Wong, Kam-Fai
Zhou, Guodong
Keywords: Dissimilarity measure
Feature space
Content-based image retrieval
Issue Date: 2008
Publisher: Springer
Citation: LIU, H., SONG, D., RUGER, S., HU, R. AND UREN, V., 2008. Comparing dissimilarity measures for content-based image retrieval. In: H. LI, T. LIU, W-Y. MA, T. SAKAI, K-F. WONG and G. ZHOU, eds. Information Retrieval Technology: 4th Asia Information Retrieval Symposium, AIRS 2008, Harbin, China, January 15-18, 2008; Revised Selected Papers. Berlin: Springer. pp. 44-50.
Series/Report no.: Lecture Notes in Computer Science
4993
Abstract: Dissimilarity measurement plays a crucial role in contentbased 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.
ISBN: 9783540686330
Appears in Collections:Book chapters (Computing)

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