An Efficient Method for Texture Feature Extraction and Recognition based on Contourlet Transform and Canonical Correlation Analysis

Authors

  • Ali Mohsin Al-juboori, dr Multimedia Department/College of Computer Science and Information Technology/University of Al-Qadisiyah

DOI:

https://doi.org/10.31185/eduj.Vol1.Iss29.167

Keywords:

: Texture features, Contourlet transform, Canonical Correlation Analysis.

Abstract

        Feature extraction is an important processing step in texture classification. For feature extraction in contourlet domain, statistical features for blocks of subband are computed. In this paper, we present an efficient feature vector extraction method for texture classification. For more discriminative feature a canonical correlation analysis method is propose for feature vector fused to the different sample of  texture in the same cluster. The KNN (K-Nearest Neighbor) classifier is utilizing to perform texture classification.

 

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Downloads

Published

2018-01-16

How to Cite

Mohsin Al-juboori, A. (2018). An Efficient Method for Texture Feature Extraction and Recognition based on Contourlet Transform and Canonical Correlation Analysis. Journal of Education College Wasit University, 1(29), 498- 511. https://doi.org/10.31185/eduj.Vol1.Iss29.167

Issue

Section

Articles