Comparing dissimilarity measures for content-based image retrieval

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

doi:10.1007/978-3-540-68636-1_5

Comparing dissimilarity measures for content-based image retrieval

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

^*Corresponding author for this work

Open University Milton Keynes

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

63 Citations (Scopus)

Abstract

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 language	English
Title of host publication	Information Retrieval Technology - 4th Asia Information Retrieval Symposium, AIRS 2008, Revised Selected Papers
Pages	44-50
Number of pages	7
DOIs	https://doi.org/10.1007/978-3-540-68636-1_5
Publication status	Published - 2008
Externally published	Yes
Event	4th Asia Information Retrieval Symposium, AIRS 2008 - Harbin, China Duration: 15 Jan 2008 → 18 Jan 2008

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4993 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	4th Asia Information Retrieval Symposium, AIRS 2008
Country/Territory	China
City	Harbin
Period	15/01/08 → 18/01/08

Keywords

Content-based image retrieval
Dissimilarity measure
Feature space

Access to Document

10.1007/978-3-540-68636-1_5

Cite this

Liu, H., Song, D., Rüger, S., Hu, R., & Uren, V. (2008). Comparing dissimilarity measures for content-based image retrieval. In Information Retrieval Technology - 4th Asia Information Retrieval Symposium, AIRS 2008, Revised Selected Papers (pp. 44-50). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4993 LNCS). https://doi.org/10.1007/978-3-540-68636-1_5

Liu, Haiming ; Song, Dawei ; Rüger, Stefan et al. / Comparing dissimilarity measures for content-based image retrieval. Information Retrieval Technology - 4th Asia Information Retrieval Symposium, AIRS 2008, Revised Selected Papers. 2008. pp. 44-50 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Comparing dissimilarity measures for content-based image retrieval",

abstract = "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.",

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note = "4th Asia Information Retrieval Symposium, AIRS 2008 ; Conference date: 15-01-2008 Through 18-01-2008",

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Liu, H, Song, D, Rüger, S, Hu, R & Uren, V 2008, Comparing dissimilarity measures for content-based image retrieval. in Information Retrieval Technology - 4th Asia Information Retrieval Symposium, AIRS 2008, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4993 LNCS, pp. 44-50, 4th Asia Information Retrieval Symposium, AIRS 2008, Harbin, China, 15/01/08. https://doi.org/10.1007/978-3-540-68636-1_5

Comparing dissimilarity measures for content-based image retrieval. / Liu, Haiming; Song, Dawei; Rüger, Stefan et al.
Information Retrieval Technology - 4th Asia Information Retrieval Symposium, AIRS 2008, Revised Selected Papers. 2008. p. 44-50 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4993 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Song, Dawei

AU - Rüger, Stefan

AU - Hu, Rui

AU - Uren, Victoria

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N2 - 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.

AB - 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.

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Liu H, Song D, Rüger S, Hu R, Uren V. Comparing dissimilarity measures for content-based image retrieval. In Information Retrieval Technology - 4th Asia Information Retrieval Symposium, AIRS 2008, Revised Selected Papers. 2008. p. 44-50. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-68636-1_5

Comparing dissimilarity measures for content-based image retrieval

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