彩色纹理分析中的多特征数据融合方法

Multiple Feature Data Fusion Method in Color Texture Analysis

在小波分解基础上将纹理特征、颜色特征及纹理与颜色的空间相关特征进行融合,提出了一种新颖的彩色纹理特征提取方法,同时结合20类真实彩色自然纹理,针对塔式小波分解(PWD),不完全树型小波分解(ICTSWD)和小波包分解(WPD)进行了多特征融合和分类比较,实验结果表明:塔式小波分解基础上的多特征融合,其正确分类率为85.78％;小波包分解基础上的多特征融合,其正确分类率为91.03％,但其特征维数呈指数增长;而不完全树型小波分解有选择地进行通道分解,其维数大大下降,多特征融合后的正确分类率达到90.63％,同时也表现出良好的抗噪能力。

A new algorithm is presented to extract colored texture by effectively merging the texture feature, color feature and spatial correlation of color texture based on wavelet decomposition. Experiments are conducted on a set of 20 natural colored texture images in which multiple feature fusion and classification can be performed on the basis of the pyramid wavelet decomposition (PWD), incomplete tree-structured wavelet decomposition (ICTSWD) and wavelet packet decomposition (WPD). It is demonstrated that correct class rate of multiple feature fusion based on PWD is 85. 78% and correct class rate based on WPD is 91.03% with the dimensionality increased exponentially, but the dimensionality of feature fusion based on ICTSWD descended greatly because of selective decomposition in sub-band, and correct class rate is 90. 63% after fusion, simultaneously, multiple feature fusion based on ICTSWD has better anti-noise ability than fusion using WPD.