Joint calibration of Ensemble of Exemplar SVMs

We present a method for calibrating the Ensemble of Exemplar SVMs model. Unlike the standard approach, which calibrates each SVM independently, our method optimizes their joint performance as an ensemble. We formulate joint calibration as a constrained optimization problem and de- vise an efficient optimization algorithm to find its global optimum. It dynamically discards parts of the solution space that cannot contain the optimum early on, making the optimization computationally feasible. Experiments on the ILSVRC 2014 dataset shows that (i) our joint calibration procedure outperforms independent calibration on the task of classifying windows as belonging to an object class or not; and (ii) this better window classifier leads to better performance on the object detection task.