Distributional clustering for efficient content-based retrieval of images and video

Author

Iyengar, Giridharan ; Lippman, Andrew B.

Author_Institution

IBM Thomas J. Watson Res. Center, Yorktown Heights, NY, USA

Volume

1

fYear

2000

fDate

2000

Firstpage

81

Abstract

We present an approach to clustering images for efficient retrieval using relative entropy. We start with the assumption that visual features are represented by probability densities and develop clustering algorithms for probability densities (for example, normalized histograms are crude approximations of probability densities). These clustering algorithms are then used for efficient retrieval of images and video

Keywords

content-based retrieval; entropy; feature extraction; image representation; image retrieval; pattern clustering; probability; video signal processing; clustering algorithms; content-based image retrieval; content-based video retrieval; distributional clustering; image databases; normalized histograms; probability densities; relative entropy; visual features representation; Clustering algorithms; Content based retrieval; Data security; Entropy; Histograms; Image databases; Image retrieval; Image storage; Laboratories; Partitioning algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 2000. Proceedings. 2000 International Conference on

Conference_Location

Vancouver, BC

ISSN

1522-4880

Print_ISBN

0-7803-6297-7

Type

conf

DOI

10.1109/ICIP.2000.900897

Filename

900897