title: Style context with second-order statistics.
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.
authors.affiliation: Automated Reasoning Division, Istituto per la Ricerca Scientifica e Tecnologica, Via Sommarive 18, Povo, Trento 38050, Italy. sriharsha@itc.it
authors.forename: Sriharsha
authors.initials: S
authors.lastname: Veeramachaneni
authors.forename: George
authors.initials: G
authors.lastname: Nagy
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003196:Computer Graphics; D016002:Discriminant Analysis; D004282:Documentation; D001330:Electronic Data Processing / Q000379:methods*; D006236:Handwriting; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D015233:Models, Statistical; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D011932:Reading; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013382:Subtraction Technique; D014584:User-Computer Interface