New hierarchical SVM classifier for multi-class target recognition

Abstract

We propose a binary hierarchical classifier to solve the multi-class classification problem with aspect variations in objects and with rejection of non-object false targets. The hierarchical architecture design is automated using our new k-means SVRM (support vector representation machine) clustering algorithm. At each node in the hierarchy, we use a new SVRDM (support vector representation and discrimination machine) classifier, which has good generalization and offers good rejection ability. We also provide a theoretical basis for our choice of kernel function (K), and our method of parameter selection (for σ and p ). Using this hierarchical SVRDM classifier with magnitude Fourier transform features, experimental results on both simulated and real infra-red (IR) databases are excellent.