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

Research output: Chapter in Book/Published conference output › Conference publication

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
Subtitle of host publication	4th Asia Infomation Retrieval Symposium, AIRS 2008, Harbin, China, January 15-18, 2008 Revised Selected Papers
Editors	Hang Li, Ting Liu, Wei-Ying Ma, Tetsuya Sakai, Kam-Fai Wong, Guodong Zhou
Place of Publication	Berlin (DE)
Publisher	Springer
Pages	44-50
Number of pages	7
Volume	4993 LNCS
DOIs	https://doi.org/10.1007/978-3-540-68636-1_5
Publication status	Published - 2008
Event	4th Asia Infomation Retrieval Symposium - Harbin, China Duration: 15 Jan 2008 → 18 Jan 2008

Publication series

Name	Lecture notes in computer science
Publisher	Springer
Volume	4993
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

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

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 H. Li, T. Liu, W.-Y. Ma, T. Sakai, K.-F. Wong, & G. Zhou (Eds.), Information retrieval technology: 4th Asia Infomation Retrieval Symposium, AIRS 2008, Harbin, China, January 15-18, 2008 Revised Selected Papers (Vol. 4993 LNCS, pp. 44-50). (Lecture notes in computer science; Vol. 4993). Springer. 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 Infomation Retrieval Symposium, AIRS 2008, Harbin, China, January 15-18, 2008 Revised Selected Papers. editor / Hang Li ; Ting Liu ; Wei-Ying Ma ; Tetsuya Sakai ; Kam-Fai Wong ; Guodong Zhou. Vol. 4993 LNCS Berlin (DE) : Springer, 2008. pp. 44-50 (Lecture notes in computer science).

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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{\textquoteright}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.",

author = "Haiming Liu and Dawei Song and Stefan R{\"u}ger and Rui Hu and Victoria Uren",

year = "2008",

doi = "10.1007/978-3-540-68636-1_5",

language = "English",

volume = "4993 LNCS",

series = "Lecture notes in computer science",

publisher = "Springer",

pages = "44--50",

editor = "Hang Li and Ting Liu and Wei-Ying Ma and Tetsuya Sakai and Kam-Fai Wong and Guodong Zhou",

booktitle = "Information retrieval technology",

address = "Germany",

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

}

Liu, H, Song, D, Rüger, S, Hu, R & Uren, V 2008, Comparing dissimilarity measures for content-based image retrieval. in H Li, T Liu, W-Y Ma, T Sakai, K-F Wong & G Zhou (eds), Information retrieval technology: 4th Asia Infomation Retrieval Symposium, AIRS 2008, Harbin, China, January 15-18, 2008 Revised Selected Papers. vol. 4993 LNCS, Lecture notes in computer science, vol. 4993, Springer, Berlin (DE), pp. 44-50, 4th Asia Infomation Retrieval Symposium, 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 Infomation Retrieval Symposium, AIRS 2008, Harbin, China, January 15-18, 2008 Revised Selected Papers. ed. / Hang Li; Ting Liu; Wei-Ying Ma; Tetsuya Sakai; Kam-Fai Wong; Guodong Zhou. Vol. 4993 LNCS Berlin (DE): Springer, 2008. p. 44-50 (Lecture notes in computer science; Vol. 4993).

Research output: Chapter in Book/Published conference output › Conference publication

TY - GEN

T1 - Comparing dissimilarity measures for content-based image retrieval

AU - Liu, Haiming

AU - Song, Dawei

AU - Rüger, Stefan

AU - Hu, Rui

AU - Uren, Victoria

PY - 2008

Y1 - 2008

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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M3 - Conference publication

VL - 4993 LNCS

T3 - Lecture notes in computer science

SP - 44

EP - 50

BT - Information retrieval technology

A2 - Li, Hang

A2 - Liu, Ting

A2 - Ma, Wei-Ying

A2 - Sakai, Tetsuya

A2 - Wong, Kam-Fai

A2 - Zhou, Guodong

PB - Springer

CY - Berlin (DE)

T2 - 4th Asia Infomation Retrieval Symposium

Y2 - 15 January 2008 through 18 January 2008

ER -

Liu H, Song D, Rüger S, Hu R, Uren V. Comparing dissimilarity measures for content-based image retrieval. In Li H, Liu T, Ma WY, Sakai T, Wong KF, Zhou G, editors, Information retrieval technology: 4th Asia Infomation Retrieval Symposium, AIRS 2008, Harbin, China, January 15-18, 2008 Revised Selected Papers. Vol. 4993 LNCS. Berlin (DE): Springer. 2008. p. 44-50. (Lecture notes in computer science). doi: 10.1007/978-3-540-68636-1_5

Comparing dissimilarity measures for content-based image retrieval

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Comparing dissimilarity measures for content-based image retrieval

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