Marginal distribution covariance model in the multiple wavelet domain for texture representation

Authors：Li, Chaorong^*; Huang, Yuanyuan; Yang, Xingchun; Chen, Huafu

Source：Pattern Recognition, 2019, 92: 246-257.

DOI：10.1016/j.patcog.2019.04.003

• Texture representation
• Covariance matrix
• Gabor wavelet
• Dual tree complex wavelet
• Orthogonal wavelet
• Convolutional neural network

Filters are the commonly used techniques for texture feature extraction. In this paper, we present a texture feature extraction method based on multiple wavelet filters. For modeling the multiple wavelet coefficients, we develop Marginal Distribution Covariance Model (MDCM) in which the data points are projected into Cumulative Distribution CDF) space and then the covariance model is constructed in the CDF space. MDCM can capture the dependence of variables, so it can be applied to model the texture features, in which the dependence exists, such as image intensities, color features and wavelet filter features. According to the characteristics of different wavelet filter features, we construct the different MDCMs in the three wavelet transform domains: Orthogonal Wavelet Transform (OWT), Dual Tree Complex Wavelet Transform (DTCWT) and Gabor Wavelet Transform (GWT). Experiments show the proposed method which uses multiple wavelet features can provide a promising performance compared with the state-of-the-art methods.

• Institution
  成都信息工程学院; 宜宾学院; 电子科技大学