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Style Context with Second-Order Statistics
January 2005 (vol. 27 no. 1)
pp. 14-22
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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Index Terms:
Interpattern feature dependence, writer consistency, continuous styles, quadratic discriminant classifier.
Citation:
Sriharsha Veeramachaneni, George Nagy, "Style Context with Second-Order Statistics," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 1, pp. 14-22, Jan. 2005, doi:10.1109/TPAMI.2005.19
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