TY - GEN
T1 - Rotation invariant texture classification based on a directional filter bank
AU - Duan, Rong
AU - Man, Hong
AU - Chen, Ling
PY - 2004
Y1 - 2004
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=11244281884&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=11244281884&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:11244281884
SN - 0780386035
SN - 9780780386037
T3 - 2004 IEEE International Conference on Multimedia and Expo (ICME)
SP - 1291
EP - 1294
BT - 2004 IEEE International Conference on Multimedia and Expo (ICME)
T2 - 2004 IEEE International Conference on Multimedia and Expo (ICME)
Y2 - 27 June 2004 through 30 June 2004
ER -