A new affine-invariant image matching method based on SIFT

摘要

Local invariant feature extraction, as one of the main problems in the field of computer vision, has been widely applied to image matching, splicing and target recognition etc. Lowe's scale invariant feature transform (known as SIFT) algorithm has attracted much attention due to its invariance to scale, rotation and illumination. However, SIFT is not robust to affine deformations, because it is based on the DoG detector which extracts keypoints in a circle region. Besides, the feature descriptor is represented by a 128-dimensional vector, which means that the algorithm complexity is extremely large especially when there is a great quantity of keypoints in the image. In this paper, a new feature descriptor, which is robust to affine deformations, is proposed. Considering that circles turn to be ellipses after affine deformations, some improvements have been made. Firstly, the Gaussian image pyramids are constructed by convoluting the source image and the elliptical Gaussian kernel with two volatile parameters, orientation and eccentricity. In addition, the two parameters are discretely selected in order to imitate the possibilities of the affine deformation, which can make sure that anisotropic regions are transformed into isotropic ones. Next, all extreme points can be extracted as the candidates for the affine-invariant keypoints in the image pyramids. After accurate keypoints localization is performed, the secondary moment of the keypoints' neighborhood is calculated to identify the elliptical region which is affine-invariant, the same as SIFT, the main orientation of the keypoints can be determined and the feature descriptor is generated based on the histogram constructed in this region. At last, the PCA method for the 128-dimensional descriptor's reduction is used to improve the computer calculating efficiency. The experiments show that this new algorithm inherits all SIFT's original advantages, and has a good resistance to affine deformations; what's more, it is more effective in calculation and storage requirement.