Comparing dissimilarity measures for content-based image retrieval.
Liu, Haiming; Song, Dawei; Rüger, Stefan; Hu, Rui; Uren, Victoria
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.
|Start Date||Jan 15, 2008|
|Publication Date||Dec 31, 2008|
|Publisher||Springer (part of Springer Nature)|
|Series Title||Lecture notes in computer science|
|Institution Citation||LIU, H., SONG, D., RUGER, S., HU, R. and UREN, V. 2008. Comparing dissimilarity measures for content-based image retrieval. In Li, H., Liu, T., Ma, W.-Y., Sakai, T., Wong, K.-F. and Zhou, G. (eds.) Information retrieval technology: revised selected papers from the proceedings of the 4th Asia information retrieval symposium (AIRS 2008), 15-18 January 2008, Harbin, China. Lecture notes in computer science, 4993. Berlin: Springer [online], pages 44-50. Available from: https://doi.org/10.1007/978-3-540-68636-1_5|
|Keywords||Dissimilarity measure; Feature space; Content based image retrieval|
LIU 2008 Comparing dissimilarity measures
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