In the previous chapter, we learned how to detect special points in an image with the objective of subsequently performing local image analysis. These keypoints are chosen to be distinctive enough such that if a keypoint is detected on the image of an object, then the same point is expected to be detected in other images depicting the same object. We also described some more sophisticated interest point detectors that can assign a representative scale factor and/or an orientation to a keypoint. As we will see in this recipe, this additional information can be useful to normalize scene representations with respect to viewpoint variations.

In order to perform image analysis based on interest points, we now need to build rich representations that uniquely describe each of these keypoints. This chapter looks at the different approaches that have been proposed to extract descriptors from interest points. These descriptors are generally 1D or 2D vectors of binary, integer, or floating...