A Hybrid IMM/SVM Approach for Wavelet-Domain Probabilistic Model Based Texture Classification

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.

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