Ensemble of furthest subspace pairs for enhanced image set matching

Recently it has been shown that the performance of image set matching methods can be improved by clustering set samples into smaller and more coherent groups. Typically, set samples are treated independently during clustering, ie., clustering criteria have not been defined to exploit set characteristics. In this paper we introduce a novel approach to image set clustering by considering the similarities between subspaces instead of similarities between samples. We exploit an ensemble learning technique to create an ensemble of subspace pairs. Each pair has the property that its members are located at the furthest distance in the sense of distances between subspaces. Object recognition experiments on the CMU-MoBO and ETH-80 datasets show that the proposed method obtains higher discrimination accuracy in comparison to several benchmark methods as well as the recently proposed Kernel Affine Hull Method.