Locating binary features for keypoint recognition using noncooperative games

Many applications in computer vision rely on determining the correspondence between two images that share an overlapping region. One way to establish this correspondence is by matching binary keypoint descriptors from both images. Although, these descriptors are efficiently computed with bits produced by an arrangement of binary features (pattern), their matching performance falls short in comparison with other more elaborated descriptors such as SIFT. We present an approach based on noncooperative game theory for computing the locations of every binary feature in a pattern, improving the performance of binary-feature-based matchers. We propose a simultaneous two-player zero-sum game in which a maximizer wants to increase a payoff by selecting the possible locations for the features; a minimizer wants to decrease the payoff by selecting a pair of keypoints to confuse the maximizer; and the payoff matrix is computed from the pixel intensities across the pixel neighborhood of the keypoints. We use the best locations from the obtained maximizer´s optimal policy for locating every binary feature in the pattern. Our evaluation of this approach coupled with Ferns shows an improvement in matching keypoints, in particular those with similar texture. Moreover, our approach improves the matching performance when fewer bits are required.