Abstract
Zepeda and Perez [41] have recently demonstrated the ´
promise of the exemplar SVM (ESVM) as a feature encoder
for image retrieval. This paper extends this approach in
several directions: We first show that replacing the hinge
loss by the square loss in the ESVM cost function signifi-
cantly reduces encoding time with negligible effect on accuracy. We call this model square-loss exemplar machine,
or SLEM. We then introduce a kernelized SLEM which can
be implemented efficiently through low-rank matrix decomposition, and displays improved performance. Both SLEM
variants exploit the fact that the negative examples are fixed,
so most of the SLEM computational complexity is relegated
to an offline process independent of the positive examples.
Our experiments establish the performance and computational advantages of our approach using a large array of
base features and standard image retrieval datasets