关键词: Contourlet变换, 特征选择, 纹理识别, 高频能量, 支持向量机

Abstract: How to identify the most appropriate feature vector is the key of image texture recognition. Considering the characteristics of Contourlet transform, the image was transformed from the spatial domain to the frequency domain. The feature vectors of low-frequency subband, medium-frequency subband and high-frequency subband were extracted comprehensively and entered to Support Vector Machine (SVM) for classification. Brodatz database was used for simulation. The experimental results demonstrate that mean and variance of low frequency and the energy of high frequency are the optimal representation of the image texture. They are combined to make the recognition accuracy rate up to 98.75% and the vector dimension is lower.

Key words: Contourlet transform, feature selection, texture recognition, high-frequency energy, Support Vector Machine (SVM)