A comparative study of evidence combination strategies

Author

Yavlinsky, Alexei ; Pickering, Marcus J. ; Heesch, Daniel ; Ruger, Stefan

Author_Institution

Dept. of Comput., Imperial Coll. London, UK

Volume

3

fYear

2004

fDate

17-21 May 2004

Abstract

The paper reports on experimental results obtained from a performance comparison of feature combinations strategies in content based image retrieval. The use of support vector machines is compared to CombMIN, CombMAX, CombSUM and BordaFuse combination strategies, all of which are evaluated on a carefully compiled set of Corel images and the TRECVID 2003 search task collection.

Keywords

content-based retrieval; feature extraction; image classification; image colour analysis; image retrieval; image texture; support vector machines; video signal processing; BordaFuse; CombMAX; CombMIN; CombSUM; Corel images; SVM meta-classification; colour features; content based image retrieval; content based retrieval; evidence combination strategies; feature combinations strategies; support vector machines; texture features; Content based retrieval; Educational institutions; Feedback; Histograms; Image retrieval; Information retrieval; Pixel; Speech recognition; Support vector machines; Tiles;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP '04). IEEE International Conference on

ISSN

1520-6149

Print_ISBN

0-7803-8484-9

Type

conf

DOI

10.1109/ICASSP.2004.1326726

Filename

1326726