Moment features in directional subband domain for rotation invariant texture classification

Hong Man, Rong Duan

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

1 Scopus citations

Abstract

This paper presents a study on moment features in directional subband domain for rotation invariant texture image classification. The directional subband decomposition is obtained through a biorthogonal angular filter bank. Moment features are extracted from each directional subband. Two rotation invariant feature generation techniques are examined, including eigenanalysis of covariance matrix and DFT encoding. Feature vectors are further classified by multiclass linear discriminant analysis (LDA). LDA training is based on feature vectors collected from non-rotated training images, and test is performed on images rotated at various angles. Experimental results are provided to demonstrate the effectiveness of directional subband domain feature extraction method for rotation invariant classification. Performance of various feature sets are compared, and the best feature combination is presented.

Original languageEnglish
Title of host publication2005 IEEE 7th Workshop on Multimedia Signal Processing, MMSP 2005
DOIs
StatePublished - 2005
Event2005 IEEE 7th Workshop on Multimedia Signal Processing, MMSP 2005 - Shanghai, China
Duration: 30 Oct 20052 Nov 2005

Publication series

Name2005 IEEE 7th Workshop on Multimedia Signal Processing

Conference

Conference2005 IEEE 7th Workshop on Multimedia Signal Processing, MMSP 2005
Country/TerritoryChina
CityShanghai
Period30/10/052/11/05

Fingerprint

Dive into the research topics of 'Moment features in directional subband domain for rotation invariant texture classification'. Together they form a unique fingerprint.

Cite this