All above descriptors are distinctive and invariant to some image transformations. The feature-based matching algorithms can be divided into three main steps. First, feature detection—for example of feature point, the interest points with the property of high repeatability are selected at distinctive locations in the image. Second, feature points description—the neighborhood of every interest point is represented by a feature vector, and there are lots of possible descriptors that emphasize a diverse set of image properties such as pixel intensity, gradient, color, texture, contour, edge, and so on. Furthermore, the descriptor has to be distinctive and robust to noise. Finally, the descriptor vectors are matched between different images. A fewer number of dimensions are therefore desirable.9 But the matched number of the key points cannot be too few or too many. A lack of matched key points will decrease the accuracy of matching, whereas too many will cost computational time. Because of these facts, one has to strike a balance between the above requirements, for example, by reducing the descriptor’s dimension and complexity, while keeping it sufficiently distinctive.