摘要
Empirical mode decomposition (EMD) is an adaptive and approximately orthogonal filtering process that reflects human’s visual mechanism of differentiating textures. In this paper, we present a modified 2D EMD algorithm using the FastRBF and an appropriate number of iterations in the shifting process (SP), then apply it to texture classification. Rotation-invariant texture feature vectors are extracted using auto-registration and circular regions of magnitude spectra of 2D fast Fourier transform (FFT). In the experiments, we employ a Bayesion classifier to classify a set of 15 distinct natural textures selected from the Brodatz album. The experimental results, based on different testing datasets for images with different orientations, show the effectiveness of the proposed classification scheme.
Empirical mode decomposition （EMD） is an adaptive and approximately orthogonal filtering process that reflects human＇s visual mechanism of differentiating textures. In this paper, we present a modified 2D EMD algorithm using the FastRBF and an appropriate number of iterations in the shifting process （SP）, then apply it to texture classification. Rotation-invariant texture feature vectors are extracted using auto-registration and circular regions of magnitude spectra of 2D fast Fourier transform （FFT）. In the experiments, we employ a Bayesion classifier to classify a set of 15 distinct natural textures selected from the Brodatz album. The experimental results, based on different testing datasets for images with different orientations, show the effectiveness of the proposed classification scheme.
基金
Project supported by the National Basic Research Program (973) of China (Nos. 2004CB318000 and 2002CB312104), the National Natural Science Foundation of China (Nos. 60133020 and 60325208) and the Natural Science Foundation of Beijing (No. 1062006), China
关键词
EMD
FFT
特征分类
傅立叶变换
自动登录
Texture classification, Empirical mode decomposition （EMD）, Fourier transform, Auto-registration, Rotationinvariant
