SIFT( Scale Invariant Feature Transform)描述符由于具有尺度、旋转和光照不变等特性在图像匹配领域获得了广泛的应用.但是,SIFT特征点采用128维特征向量表示,当图像特征点较多时,匹配算法所需的存储空间大、匹配时间长,且匹配精度不理想.针对以上问题,本文给出了一种基于Rough-SIFT描述符的图像匹配算法.首先,利用排序法求出图像的稳健特征点,然后为提高后续匹配处理运算效率,将粗糙集约简理论引入到基于SIFT特征的匹配算法中,通过构建一种新的近似约简算法来对稳健特征点的128维特征向量进行降维处理,最后利用约简后的特征点对图像进行匹配.仿真实验表明,本文方法使得约简后的SIFT特征点更加精确、稳定、可靠,有效减小了匹配算法的存储空间,提高了匹配算法的效率和准确率.%For the rotation, translation, scale invariant properties of SIFT (Scale Invariant Feature Transform) feature, it has been widely applied in image matching. However, it is represented by a 128 element feature vector, and when it is used for image matching, especially for the case that there are many keypoints in the image, the matching speed will be slow and storage requirement will be huge, and matching precision is low. In order to overcome these disadvantages, a new image matching algorithm based on Rough-SIFT descriptor is proposed in this paper. Firstly, we select the robust and salient SIFT features according to ranking the local invariant features. Then the rough set theory is introduced into image matching by putting forward a new approximation reduction algorithm which is used to reduce the dimension of SIFT feature vector. At last, the reduced feature points are used to image matching. Some experimental results have been provided to show the proposed method not only effectively realizes image matching, but also has higher matching speed and lower storage requirement.
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