Sarkar Prateek, Nagy George
Palo Alto Research Center, 3333 Coyote Hill Road, Palo Alto, CA 94304, USA.
IEEE Trans Pattern Anal Mach Intell. 2005 Jan;27(1):88-98. doi: 10.1109/TPAMI.2005.18.
In many applications of pattern recognition, patterns appear together in groups (fields) that have a common origin. For example, a printed word is usually a field of character patterns printed in the same font. A common origin induces consistency of style in features measured on patterns. The features of patterns co-occurring in a field are statistically dependent because they share the same, albeit unknown, style. Style constrained classifiers achieve higher classification accuracy by modeling such dependence among patterns in a field. Effects of style consistency on the distributions of field-features (concatenation of pattern features) can be modeled by hierarchical mixtures. Each field derives from a mixture of styles, while, within a field, a pattern derives from a class-style conditional mixture of Gaussians. Based on this model, an optimal style constrained classifier processes entire fields of patterns rendered in a consistent but unknown style. In a laboratory experiment, style constrained classification reduced errors on fields of printed digits by nearly 25 percent over singlet classifiers. Longer fields favor our classification method because they furnish more information about the underlying style.
在模式识别的许多应用中，模式以具有共同起源的组（域）的形式一起出现。例如，一个印刷单词通常是一组以相同字体印刷的字符模式。共同起源会在模式上测量的特征中引发风格的一致性。在一个域中共同出现的模式的特征在统计上是相关的，因为它们共享相同的（尽管未知的）风格。风格受限分类器通过对一个域中模式之间的这种相关性进行建模来实现更高的分类准确率。风格一致性对域特征（模式特征的串联）分布的影响可以通过分层混合来建模。每个域源自风格的混合，而在一个域内，一个模式源自高斯类风格条件混合。基于此模型，一个最优的风格受限分类器处理以一致但未知的风格呈现的整个模式域。在一项实验室实验中，与单类分类器相比，风格受限分类将印刷数字域上的错误减少了近25%。更长的域有利于我们的分类方法，因为它们提供了更多关于潜在风格的信息。
