TY - GEN
T1 - A hybrid IMM/SVM approach for wavelet-domain probabilistic model based texture classification
AU - Chen, Ling
AU - Man, Hong
PY - 2005
Y1 - 2005
N2 - Fisher kernel method was recently proposed to incorporate probabilistic (generative) models and discriminative methods for pattern recognition (PR). This method use parameter derivatives of log-likelihood calculated from probabilistic model(s), "Fisher scores", to generate statistical feature vectors. It is followed by discriminative classifiers such as "support vector machine" (SVM) for classification. In this work we study the potential of Fisher kernel method on texture classification. A hybrid system of "independent mixture model" (IMM) and SVM is introduced to extract and classify statistical texture features in wavelet-domain. Compared to existing methods that apply Bayesian classification based on wavelet domain "energy signatures" (ES) and stand along IMM, the new hybrid IMM/SVM method is able to achieve superior performance. Experimental results are presented to demonstrate the effectiveness of this proposed method.
AB - Fisher kernel method was recently proposed to incorporate probabilistic (generative) models and discriminative methods for pattern recognition (PR). This method use parameter derivatives of log-likelihood calculated from probabilistic model(s), "Fisher scores", to generate statistical feature vectors. It is followed by discriminative classifiers such as "support vector machine" (SVM) for classification. In this work we study the potential of Fisher kernel method on texture classification. A hybrid system of "independent mixture model" (IMM) and SVM is introduced to extract and classify statistical texture features in wavelet-domain. Compared to existing methods that apply Bayesian classification based on wavelet domain "energy signatures" (ES) and stand along IMM, the new hybrid IMM/SVM method is able to achieve superior performance. Experimental results are presented to demonstrate the effectiveness of this proposed method.
UR - http://www.scopus.com/inward/record.url?scp=35348834795&partnerID=8YFLogxK
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U2 - 10.1109/ACVMOT.2005.7
DO - 10.1109/ACVMOT.2005.7
M3 - Conference contribution
AN - SCOPUS:35348834795
SN - 0769522718
SN - 9780769522715
T3 - Proceedings - Seventh IEEE Workshop on Applications of Computer Vision, WACV 2005
SP - 281
EP - 286
BT - Proceedings - Seventh IEEE Workshop on Applications of Computer Vision, WACV 2005
T2 - 7th IEEE Workshop on Applications of Computer Vision, WACV 2005
Y2 - 5 January 2005 through 7 January 2005
ER -