基于不同复小波变换方法的纹理检索和相似计算

复小波克服了单小波的缺点，具有时移不变性、方向性信息多和相位信息等特点．从能量角度出发．主要研究了不同复小波变换方法的一阶和二阶统计矩（共生矩阵）特性，并应用于纹理特征的提取，与传统的单小波做了比较．通过理论分析和在纹理图像检索的对比实验数据说明了复小波在纹理特征提取方面的性能优于单小波，采用一阶和二阶统计矩相结合方法的性能最好，检索精度提高了8％．

Different Complex Wavelet Transforms for Texture Retrieval and Similarity Measure

Complex wavelet transform overcomes the drawbacks of discrete wavelet transform, such as shift sensitivity, poor directionality and lack of the phase information. In this paper, the performance of the first-order and the second-order (co -occurrence) statistical characters of the different complex wavelet transforms (CWT) is studied with the consideration of the wavelet energy, and applied to texture feature extraction. It is concluded that the performance of the CWT is better than the pyramid discrete wavelet decomposition transforms (PDWT) on the t exture feature extraction through theory analysis and the contrast experiments results on the texture retrieval. Best performance is achieved by combining the first-order signatures with the second-order signatures and the performance of retrieval is raised 8%.