基于非抽样Contourlet域的局部特征描述子

Local Feature Descriptor Based on Nonsubsampled Contourlet Transform Domain

为了得到鲁棒性较强的局部特征描述子,提出一种基于非抽样Contourlet(NSCT)域的局部特征描述子,该局部特征描述子在构造的过程中不需要估计主方向。首先,利用Hessian-Affine算子进行区域检测并进行规范化处理;其次,对规范化处理后的区域进行NSCT分解,得到不同尺度、不同分辨率的多个支持区域;然后,利用亮度序对支持区域进行划分;最后,对每个子区域进行描述并将每个区域的描述向量串联在一起,得到最终的特征描述子。大量的实验结果表明,提出的描述子在图像模糊变换、视角变换、仿射变换、线性亮度变化和JPEG压缩变换下具有良好的性能。

In order to obtain the robust local feature descriptor,a local feature descriptor based on nonsubsampled contourlet transform( NSCT) domain was proposed,which does not need to estimate the main direction in the construction process. Firstly,the affine invariant region was detected using Hessian-Affine detector and then was normalized. Secondly,the normalized region was decomposed by NSCT to get multiple support regions of different scales and resolutions. Thirdly,the support regions were divided based on intensity orders. Finally,each sub-region was described as a vector and the local feature descriptor was obtained by aggregating the vector of each sub-region. The experimental results showed the good effectiveness of the proposed descriptor for the image with blurring,viewpoint changes,affine changes,linear illumination changes and JPEG compression changes.