Flexible rate allocation for local binary feature compression

Numerous real-time applications in computer vision rely on finding correspondences between local binary features. In many mobile scenarios, the visual information captured at a sensor node needs to be transmitted to a processing server, which is capable of storing the visual information or executing a complex analysis task. However, not necessarily all the visual information need to be transmitted. In this paper, we present a rate allocation scheme that is capable of categorizing features into classes according to their usefulness and select the amount of data spent on each class to maximize the overall performance of a computer vision task. We demonstrate the approach using ORB, BRISK, and FREAK features and show the improvements on a homography estimation task.