Soft-decision hierarchical classification using SVM-type classifiers

Abstract

In this paper, we address both recognition of true object classes and rejection of false (non-object) classes as occurs in many realistic pattern recognition problems. We modified our hierarchical binary-decision classifier to produce analog outputs at each node, with values proportional to the class conditional probabilities at that node. This yields a new soft-decision hierarchical system. The hierarchical classification structure is designed by our weighted support vector k-means clustering method, which selects the classes to be separated at each node in the hierarchy. Use of our SVRDM (support vector representation and discrimination machine) classifiers at each node provides generalization and rejection ability. Compared to the standard SVM, use of the Gaussian kernel function and a looser constraint in the classifier design give our SVRDM an improved rejection ability. The soft-decision SVRDM output allows us to use the confidence level of each class to improve the classification (for true class inputs) and rejection (for false class inputs) performance of the hierarchical classifier. False class rejection is a major new aspect of this work. It is not present in most prior work. Excellent test results on a real infra-red (IR) database are presented.