The scale-invariant features are efficiently identified by using a staged filtering approach. The first stage identifies key locations in scale space by looking for locations that aremaxima orminima of a difference-of-Gaussian function.Each point is used to generate a feature vector that describes the local image region sampled relative to its scale-space coordinate frame. The features achieve partial invariance to local variations, such as affine or 3D projections, by blurring image gradient locations. This approach is based on a model of the behavior of complex cells in the cerebral cortex of mammalian vision. The resulting feature vectors are called SIFT keys. In the current implementation, each image generates on the order of 1000 SIFT keys, a process that requires less than 1 second of computation time.

尺度不变的特征能够通过采用分阶段滤波的方法来有效地确定。第一阶段确定尺度空间的关键点在于在不同的高斯函数中寻找极大或极小点。每一个点产生一个特征向量来描述关于其尺度空间坐标系的局部区域采样。这个功能实现了模糊图像梯度点的局部变化的部分不变性，如仿射或三维投影。这种方法是基于哺乳动物的大脑皮层下的复合细胞行为的模型。由此而产生的特征向量被称为SIFT键。照目前的情况，每幅图像按命令产生1000个SIFT键， 这个过程只要不到1秒的计算时间。