Rotation invariant texture classification using directional filter bank and support vector machine

Hong Man, Ling Chen, Rong Duan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

This paper presents a rotation invariant texture classification method using a special directional filter bank (DFB) and support vector machine (SVM). This method extracts a set of coefficient vectors from directional subband domain, and models them as multivariate Gaussian densities. Eigen-analysis is then applied to the covariance metrics of these density functions to form rotation invariant feature vectors. Classification is based on SVM, which only takes non-rotated images for training and uses images at various rotation angles for testing. Experimental results have shown that this DFB is very effective in capturing directional information of texture images, and the proposed rotation invariant feature generation and SVM classification method can in fact achieve relatively consistent classification accuracy on both non-rotated and rotated images.

Original languageEnglish
Title of host publication2004 International Conference on Image Processing, ICIP 2004
Pages1545-1548
Number of pages4
DOIs
StatePublished - 2004
Event2004 International Conference on Image Processing, ICIP 2004 - , Singapore
Duration: 18 Oct 200421 Oct 2004

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume3
ISSN (Print)1522-4880

Conference

Conference2004 International Conference on Image Processing, ICIP 2004
Country/TerritorySingapore
Period18/10/0421/10/04

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