Greedy-Based Design of Sparse Two-Stage SVMs for Fast Classification

Cascades of classifiers constitute an important architecture for fast object detection. While boosting of simple (weak) classifiers provides an established framework, the design of similar architectures with more powerful (strong) classifiers has become the subject of current research. In this paper, we focus on greedy strategies recently proposed in the literature that allow to learn sparse Support Vector Machines (SVMs) without the need to train full SVMs beforehand. We show (i) that asymmetric data sets that are typical for object detection scenarios can be successfully handled, and (ii) that the complementary training of two sparse SVMs leads to sequential two-stage classifiers that slightly outperform a full SVM, but only need about 10% kernel evaluations for classifying a pattern.