针对彩色图像匹配大多数仅利用图像灰度特征，忽视彩色信息，在颜色相近区域易造成误匹配的问题，引入颜色不变量，提出一种用于彩色图像的颜色尺度不变匹配算法。该算法首先将图像的RGB 颜色模型转换到高斯颜色模型。随后，通过计算颜色不变量代替灰度信息作为输入，建立多尺度空间，并使用SIFT 算法提取特征点，构建特征描述子。最后，选择其中一幅图的特征描述矢量为基准，利用FANN 算法在另一幅图的特征描述矢量中，寻找基准图像特征矢量的最优解即匹配对，从而完成两幅图像的匹配。实验证明，相对于传统的SIFT 图像匹配算法，本文的算法匹配正确率更高，图像匹配质量更好。

Neglecting the color information, image grayscale is widely used in image matching. This may lead to the mismatch between similar colors in the zone. Our proposed algorithm transforms the RGB color model of the images into a Gaussian color model and calculates the color invariant to establish a multiscale space. Subsequently, using SIFT thinking as a lesson, the color scale invariant feature vector is extracted. Finally, the FANN search strategy is used to realize the precise and efficient match of the high-dimension feature vector. Our experiments results demonstrate that the proposed algorithm has obvious advantages in terms of matching point logarithm and matching accuracy.