Rotation invariant texture classification based on a directional filter bank

Rong Duan, Hong Man, Ling Chen

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

Abstract

This paper presents a rotation invariant texture classification method using a special directional filter bank (DFB). The new method extracts a set of coefficient vectors from directional subband domain, and models them with multivariate Gaussian density. Eigen-analysis is then applied to the covariance metrics of these density functions to form rotation invariant feature vectors. Classification is based on the distance between known and un-known feature vectors. Two distance measures are studied in this work, including the Kullback-Leibler distance and the Euclidean distance. 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 classification method can in fact achieve high classification accuracy on both non-rotated and rotated images.

Original languageEnglish
Title of host publication2004 IEEE International Conference on Multimedia and Expo (ICME)
Pages1291-1294
Number of pages4
StatePublished - 2004
Event2004 IEEE International Conference on Multimedia and Expo (ICME) - Taipei, Taiwan, Province of China
Duration: Jun 27 2004Jun 30 2004

Publication series

Name2004 IEEE International Conference on Multimedia and Expo (ICME)
Volume2

Conference

Conference2004 IEEE International Conference on Multimedia and Expo (ICME)
Country/TerritoryTaiwan, Province of China
CityTaipei
Period6/27/046/30/04

ASJC Scopus subject areas

  • General Engineering

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