Three Viewpoints Toward Exemplar SVM

In contrast to category-level or cluster-level classifiers, exemplar SVM is successfully applied to classifying (or detecting) a target object as well as transferring instance-level annotations. The method, however, is formulated in a highly biased classification problem where only one positive sample is contrasted with a substantial number of negative samples, which makes it difficult to properly determine the regularization parameters balancing two types of costs derived from positive and negative samples. In this paper, we present two novel viewpoints toward exemplar SVM in addition to the original definition. From these proposed viewpoints, we can give light on an intrinsic structure of exemplar SVM, reducing two parameters into only one as well as providing clear intuition on the parameter, in order to free us from exhaustive parameter tuning. We can also clarify how the classifier geometrically works so as to produce homogeneous classification scores of multiple exemplar SVMs which are comparable to each other without calibration. In addition, we propose a novel feature transformation method based on those viewpoints which contributes to general classification tasks. In the experiments on object detection and image classification, the proposed methods regarding exemplar SVM exhibit favorable performance.