Experimental Evaluation of Multiplicative Kernel SVM Classifiers for Multi-Class Detection

We consider the multi-class object detection approach based on a non-parametric multiplicative kernel, which provides both separation against backgrounds and feature sharing among foreground classes. The training is carried out through the SVM framework. According to the obtained support vectors, a set of linear detectors is constructed by plugging the foreground training samples into the multiplicative kernel. However, evaluating the complete set would be inefficient at runtime, which means that the number of detectors has to be reduced somehow. We propose to reduce that number in a novel way, by an appropriate detector selection procedure. The proposed detection approach has been evaluated on the Belgian traffic sign dataset. The experiments show that detector selection succeeds to reduce the number of detectors to the half of the number of object classes. We compare the obtained performance to the results of other detection approaches and discuss the properties of our approach.