Feature detection is the basis of image matching, it has been widely used in machine vision、aircraft navigation、image stitching、3D reconstruction and so on. Among them, KAZE algorithm based on nonlinear diffusion performs better on robustness and matching rate than others, but it is slower. To solve the above problem, a simple and effective algorithm is proposed to improve KAZE. The algorithm reduces running time by improving search strategy of feature point, using round to improve M SURF and reducing feature vector dimension, making the cosine as similarity measure. The experiments results show that the algorithm can enhance real time under the premise of ensuring original robustness and matching rate.
