基于多目标优化的SIFT特征匹配算法

SIFT Feature Matching Algorithm Based on Multi-objective Optimization

SIFT算子在实际应用中,由于地面图像本身特征不明显且提取出的特征点多、乱以及灰度变化不明显等特点的影响,从而导致特征点误匹配。为此提出一种改进的SIFT图像特征匹配算法。该算法是在SIFT特征匹配的基础上,利用多目标优化算法,建立相关匹配模板,利用给定同一场景的两幅图像,寻找同一场景点投影到图像中的模板之间的相关性建立数学模型即目标函数,根据同一幅图像中模板间的距离建立边界约束条件,从而剔除一些误匹配点。实验表明,该算法可以有效地提高图像匹配精度。

SIFT （Scale Invariant Feature Transform） descriptor is commonly used in image matching due to the invariance of scale, rotation and illumination. While in practical applications, incorrect matching of feature points was happened because the ground image＇s features were not obvious and the feature points extracted by SIFT were more, chaos as well as gray-scale did not change significantly. In order to solve the problem, an improved SIFT image feature point matching method is proposed. The method based on SIFT feature matching algorithm and used muhi objective optimization algorithm to establish correlation matching template, the two images of same scene was given to find an image projected onto a template to establish the correlation between the mathematical model of the objective function. According to distance between the template in the same image to establish the boundary constraint conditions, thus eliminate some false matching points. The experiments show that the algorithm can effectively improve its accuracy.