One Versus One (OvO) is a technique used to turn a binary classifier into a multi-class classifier. Given N classes, one classifier for every possible pairwise combination of classes is trained (a total of N*(N-1)/2 classifiers). At test time, all of them are called, generating a vote for each one-versus-one class contest, and the class with the most votes gets assigned to the sample.