KAZE结合感知哈希的图像匹配算法

Image Matching Algorithm Based on KAZE and Perceptual Hash

摘要:

针对目前图像特征匹配中存在不同程度误匹配的问题,提出了一种KAZE结合感知哈希的图像匹配算法。首先采用KAZE算法提取图像特征点并匹配,然后使用感知哈希算法筛选提纯初始匹配对,最后运用随机抽样一致(RANSAC)算法再次筛选,得到最终的匹配结果。在Mikolajczyk标准图像集上的实验结果表明,该算法在图像发生旋转缩放、光照变换、模糊以及JPEG压缩等变化下均能够保持较高的匹配正确率,具有良好的可靠性和鲁棒性。

Abstract:

An image matching algorithm based on KAZE and Perceptual Hash is proposed to solve the mismatching problem in image feature matching. Firstly, it uses KAZE algorithm to extract and match the image feature points. Then the perceptual Hash algorithm is used to filter and purify the initial matching pairs. Finally, random sampling consensus (RANSAC) algorithm is adopted to remove the false matching points, and thus the final matching results are obtained. Experiments performed on Mikolajczyk standard dataset indicate that the matching accuracy of the proposed algorithm is high with image changes in rotation, scale, illumination, blurring and JPEG compression, and has good reliability and robustness.