多方向多尺度Gabor特征表示及其匹配算法

Multi-directional and Multi-scale Gabor Feature Representation and Its Matching Algorithm

Gabor滤波是众所周知的一类特征提取方法,在机器视觉等领域得到了广泛研究和应用.本文提出了一种多方向多尺度Gabor特征表示、提取以及其匹配算法.多方向多尺度Gabor特征通过使用一组不同尺度和不同方向的Gabor滤波器对图像进行滤波,而后将滤波结果在各个滤波方向按尺度大小排序后连接而成.本文进一步提出了循环向量的概念,并将两个多方向多尺度Gabor特征相似度重新定义为一个多方向多尺度Gabor特征和对应的多个循环向量之间最大值.实验结果表明,本文提出的多方向多尺度Gabor特征不仅具有平移不变性、旋转不变性、尺度不变性,也展现出优秀的局部特征表示能力以及显著的鉴别力.

Gabor filtering is a well-known feature extraction method,which has been widely studied and applied in the field of machine vision.This paper presents a new multi-directional and multi-scale Gabor feature representation,extraction and its matching algorithm.By using a set of Gabor filters with different scales and different directions to filter an image,the filtered results in each direction are reorganized in the order of the scales and concatenated into a multi-directional and multi-scale Gabor feature.We further propose the concept of cyclic vectors and redefine a similarity measure for multi-directional and multi-scale Gabor features as the maximum similarity value between one feature vector and the corresponding cyclic vectors.Our experimental results show that the proposed descriptor not only has the characteristics of translational invariance,rotational invariance,and scale invariance,but also embody the good feature representation ability and the significant discriminative strength for the local region descriptors in image.