摘要
提出了一种用于纹理分类的旋转不变性特征提取的新算法.该算法是将一定大小的图像进行二维傅里叶变换;其次在变换后的图像中央选择一个圆盘区域,并在方向[0°,180°]内进行等间隔角度频率抽样,实现方向分解,使用一组复Morlet小波对每个方向上的映射切片进行小波变换,从而实现多通道频率分解;在各个频率通道中计算均值和方差作为特征,并利用线性回归模型计算频率通道之间的关系特征;将特征沿方向进行一维傅里叶变换并取其幅值,从而得到旋转不变性特征.实验结果表明所提取的特征具有较好的旋转不变性,与其它算法相比具有更好的分类性能,并且对无旋转纹理分类也能产生较好的分类结果.
A new rotation invariant feature extraction method for texture classification is proposed. 2-D Fourier transform is applied on a texture image,a disk area within the central region of image is chosen,and frequency is sampled on the selected area with equal interval angles within the orientation[-0°, 180°],so orientation decomposition is realized. A set of complex Morlet wavelet are applied on projection slice of each direction to decompose each projection into several frequency channels, the average and variance extracted are computed in each frequency channel, and then linear regression model is employed to computer realationship feature between frequency channels： 1-D DFT is applied to features and the amplitudes of Fourier coefficient are selected as features,then the extracted features are rotation invariant. Experimental results show that features extracted have a good rotation invariant and better classification performance with some existing methods, and better classification results can also be achieved for nonrotation texture classification.
出处
《光子学报》
EI
CAS
CSCD
北大核心
2010年第2期352-356,共5页
Acta Photonica Sinica
基金
国家自然科学基金(60705020)
国家高技术研究发展计划(2006AA04Z238)资助
关键词
脊波变换
复Morlet小波
方向-频率分解
旋转不变性
纹理特征
纹理分类
Ridgelet transform
Complex Morlet wavelets
Orientation-frequency decomposition
Rotationinvariance
Texture feature
Texture classification
