Publication | Closed Access
General methodology for simultaneous representation and discrimination of multiple object classes
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1998
Year
EngineeringMachine LearningFeature DetectionObject CategorizationBiometricsFeature ExtractionRobust FeatureImage AnalysisNonlinear Mrdf AlgorithmData SciencePattern RecognitionGeneral MethodologyMultilinear Subspace LearningNonlinear Feature ExtractionVision RecognitionMachine VisionObject DetectionSimultaneous RepresentationComputer Science3D Object RecognitionComputer VisionObject RecognitionMultiple Object ClassesPattern Recognition Application
We address a new general method for linear and nonlinear feature extraction for simultaneous representation and classification. We call this approach the maximum representation and discrimination feature (MRDF) method. We develop a novel nonlinear eigenfeature (NLEF) extraction technique to represent data with closed-form solutions and use it to derive a nonlinear MRDF algorithm. Results of the MRDF method on synthetic databases are shown and compared with results from standard Fukunaga-Koontz transform and Fisher discriminant function methods. The method is also applied to an automated product inspection problem (discrimination) and for classification and pose estimation of two similar objects under 3-D aspect angle variations (representation and discrimination).