基于扩散距离的SIFT特征匹配算法

SIFT(Scale Invariant Feature Transform)是目前最流行的局部特征提取及匹配算法.但传统SIFT算法采用欧氏距离来度量特征之间的SSD(Sum of Square Differences)并进行匹配,而传统的欧氏距离不能使高维特征向量恢复到具有低维的几何结构,导致错误匹配.为了克服这缺点,利用扩散距离代替欧氏距离进行匹配,然后使用随机抽样一致从候选匹配中排除错误的匹配.实验表明:该方法在图像形变、光照变化和图像噪声方面优于原方法.

SIFT Matching Algorithm Based on Diffusion Distance

The SIFT（Scale Invariant Feature Transform） algorithm is now regarded as the best local feature extraction and matching algorithm. However, in the traditional SIFT algorithm, the Euclidean distance which could not change the high-dimensional feature vector into a low-dimensional geometry structure is used to measure the SSD（Sum of Square Differences） between two image features to match and results into mismatching. To overcome the shortcoming, an SIFT matching algorithm based on diffusion distance is proposed in this paper which replaces the Euclidean distance with the diffusion one. At the same time, RANSAC（Random Sample Consensus） is presented to exclude the mismatching points. Experimental results show that the proposed algorithm has more efficiency to deal with image deformation, illumination chan~e and image noise than the traditional one.