Abstract

In this work, we investigate the use of exemplar SVMs (linear SVMs trained with one positive example only and a vast collection of negative examples) as encoders that turn generic image features into new, task-tailored features. The proposed feature encoding leverages the ability of the exemplar-SVM (E-SVM) classifier to extract, from the original representation of the exemplar image, what is unique about it. While existing image description pipelines rely on the intuition of the designer to encode uniqueness into the feature encoding process, our proposed approach does it explicitly relative to a “universe” of features represented by the generic negatives. We show that such a post-processing enhances the performance of state-of-the art image retrieval methods based on aggregated image features, as well as the performance of nearest class mean and K-nearest neighbor image classification methods. We establish these advantages for several features, including “traditional” features as well as features derived from deep convolutional neural nets. As an additional contribution, we also propose a recursive extension of this E-SVM encoding scheme (RESVM) that provides further performance gains.