Statistical modeling in wavelet domain for Bayesian texture classification and retrieval

Feature extraction and similarity measure in feature space are two basic steps in a texture based image retrieval system. In this paper, we propose to extract statistical modelbased features in wavelet domain where we use dual tree complex wavelet transform (DT-CWT). To this end, we employ generaliz...

Full description

Saved in:
Bibliographic Details
Main Authors: Nejadhashemi, S. L., Nasersharif, B., Shahbahrami, A.
Format: Conference Proceeding
Language:English
Subjects:
Bayesian classification
Bayesian methods
Discrete wavelet transforms
dual tree complex wavelet transform
Feature extraction
generalized Gaussian density
Support vector machine classification
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Feature extraction and similarity measure in feature space are two basic steps in a texture based image retrieval system. In this paper, we propose to extract statistical modelbased features in wavelet domain where we use dual tree complex wavelet transform (DT-CWT). To this end, we employ generalized Gaussian density (GGD) to describe the statistical characteristics of DT-CWT coefficients. On the other hand, we utilize Bayesian classifier for measuring similarity and so texture classification. In addition, for improving the classification rate and computational complexity we project the features onto a low dimensional space using three methods: linear discriminate analysis (LDA), locality preserving projections (LPP) and kernel LDA (KLDA). Our experiments are conducted on two different texture databases, i.e. VisTex and Brodatz. We achieve the classification rates up to 97.5% and 96.54% for these two databases respectively which validate the robustness of the proposed method.
DOI:10.1109/ICCKE.2011.6413354