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Style context with second-order statistics.

Sriharsha Veeramachaneni·George Nagy
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Abstract

Patterns often occur as homogeneous groups or fields generated by the same source. In multisource recognition problems, such isogeny induces statistical dependencies between patterns (termed style context). We model these dependencies by second-order statistics and formulate the optimal classifier for normally distributed styles. We show that model parameters estimated only from pairs of classes suffice to train classifiers for any test field length. Although computationally expensive, the style-conscious classifier reduces the field error rate by up to 20 percent on quadruples of handwritten digits from standard NIST data sets.

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AlgorithmsArtificial IntelligenceComputer GraphicsDiscriminant AnalysisDocumentationElectronic Data ProcessingHandwritingImage Interpretation, Computer-AssistedInformation Storage and RetrievalModels, StatisticalNumerical Analysis, Computer-AssistedPattern Recognition, AutomatedReadingReproducibility of ResultsSensitivity and SpecificitySignal Processing, Computer-AssistedSubtraction TechniqueUser-Computer InterfaceComparative StudyEvaluation StudyJournal ArticleValidation Study

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Sriharsha Veeramachaneni, George Nagy. 2005. Style context with second-order statistics.. https://doi.org/10.1109/tpami.2005.19

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