Statistical wavelet subband modelling for texture classification

Author

Hill, P.R. ; Canagarajah, C.N. ; Bull, D.R.

Author_Institution

Bristol Univ., UK

Volume

1

fYear

2001

fDate

6/23/1905 12:00:00 AM

Firstpage

165

Abstract

Simple wavelet and wavelet packet transforms have often been used for texture characterisation through the analysis of spatial-frequency content. However, most previous methods make no use of any statistical analysis of the transforms´ subbands. A novel method is now presented for modelling the multivariate distributions of subband coefficients by considering spatially related coefficients. The Bhattacharya and divergence metrics are then used to produce an improved texture classification method for the application to content based image retrieval

Keywords

content-based retrieval; discrete wavelet transforms; image classification; image retrieval; image texture; parameter estimation; probability; statistical analysis; Bhattacharya metrics; content based image retrieval; discrete wavelet transform; divergence metrics; multivariate distributions; probability density function parameters estimation; spatial-frequency content analysis; statistical analysis; statistical wavelet subband modelling; subband coefficients; texture classification; transform subbands; wavelet packet transforms; wavelet transforms; Content based retrieval; Discrete wavelet transforms; Feature extraction; Hidden Markov models; Image analysis; Image retrieval; Statistical analysis; Wavelet analysis; Wavelet packets; Wavelet transforms;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 2001. Proceedings. 2001 International Conference on

Conference_Location

Thessaloniki

Print_ISBN

0-7803-6725-1

Type

conf

DOI

10.1109/ICIP.2001.958979

Filename

958979