title: A hierarchical classifier using new support vector machines for automatic target recognition.
abstract: A binary hierarchical classifier is proposed for automatic target recognition. We also require rejection of non-object (non-target) inputs, which are not seen during training or validation, thus producing a very difficult problem. The SVRDM (support vector representation and discrimination machine) classifier is used at each node in the hierarchy, since it offers good generalization and rejection ability. Using this hierarchical SVRDM classifier with magnitude Fourier transform (|FT|) features, which provide shift-invariance, initial test results on infra-red (IR) data are excellent.
authors.affiliation: Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA. casasent@ece.cmu.edu
authors.forename: David
authors.initials: D
authors.lastname: Casasent
authors.forename: Yu-Chiang
authors.initials: YC
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D002965:Classification; D016208:Databases, Factual; D005583:Fourier Analysis; D007091:Image Processing, Computer-Assisted; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated