A cascading scheme for speeding up multiple classifier systems

Abstract

The accuracy of multi-class classification problems is improving at a good pace. However, improving the accuracy often leads to slowing down the processing speed. Since employing a large number of classifiers or a combination of them is a time-consuming process, the sluggish behavior is more evident in multiple classifier systems. In this paper, a practical cascading scheme is proposed for boosting the speed of a multiple binary classifier system with no noticeable reduction in recognition rate. The proposed cascading scheme sets a sequence of binary classifiers and applies one classifier at a time to the input. Some effective criteria for a practical ordering of classifiers are introduced, and a fusion of them is verified to be the best. A vehicle make and model recognition (VMMR) system with multiple individual classifiers is presented briefly as a use case for a multi-class classification problem. The experiments done on this VMMR system using two completely different datasets confirm the effectiveness of our scheme. One of the configurations of the proposed scheme results in up to 30% speedup in comparison with the baseline VMMR system with analogous recognition rate. Another configuration of the proposed cascading scheme achieves up to 80% speedup with just a minor drop in accuracy.