ICA系数的高阶统计特征在纹理分类中的应用

Application of Higher-Order Statistics Features of ICA Coefficients in Texture Classification

根据独立成分分析系数满足非高斯分布的特点,研究高阶统计量在特征提取中的作用.提出利用方差、偏度、峭度的联合矩描述系数的分布特征,应用于纹理分类时取得较好效果.在矩估计时进一步提出利用L-矩代替常规矩进行估计,获得更好的纹理分类效果.

ICA coefficients are non-Gaussian in independent component analysis model. The high-order statistical features are used in characterization of non-Gaussian feature. The combined moments of variance, skewness and kurtosis are proposed to describe the ICA coefficients probability distributing characteristic. The combined moments are used in texture classification and it can achieve better classification performance than the previously reported ICA features. Furthermore, L-moments are used to improve robustness in moments estimation and to get better performance than the ordinary moments.