Dissimilarity measures for content-based image retrieval

Author

Hu, Rui ; Rüger, Stefan ; Song, Dawei ; Liu, Haiming ; Huang, Zi

Author_Institution

Knowledge Media Inst., Open Univ., Milton Keynes

fYear

2008

fDate

June 23 2008-April 26 2008

Firstpage

1365

Lastpage

1368

Abstract

Dissimilarity measurement plays a crucial role in content-based image retrieval. In this paper, 16 core dissimilarity measures are introduced and evaluated. We carry out a systematic performance comparison on three image collections, Corel, Getty and Trecvid2003, with 7 different feature spaces. Two search scenarios are considered: single image queries based on the vector space model, and multi-image queries based on k-nearest neighbours search. A number of observations are drawn, which will lay a foundation for developing more effective image search technologies.

Keywords

content-based retrieval; feature extraction; image retrieval; Corel image collection; Getty image collection; Trecvid2003 image collection; content-based image retrieval; dissimilarity measures; fc-nearest neighbours search; image search; multiimage queries; single image queries; vector space model; visual feature extraction; Chebyshev approximation; Cities and towns; Content based retrieval; Entropy; Extraterrestrial measurements; Feature extraction; Image retrieval; Shape measurement; Space technology; Statistical distributions; content-based image retrieval; dissimilarity measure; feature space;

fLanguage

English

Publisher

ieee

Conference_Titel

Multimedia and Expo, 2008 IEEE International Conference on

Conference_Location

Hannover

Print_ISBN

978-1-4244-2570-9

Electronic_ISBN

978-1-4244-2571-6

Type

conf

DOI

10.1109/ICME.2008.4607697

Filename

4607697