We present an automatic method of designing correlation filters for pattern recognition that are composed of select local features (i.e., small parts of a reference object). The local features are selected for their ability to discriminate between the reference object and other known objects or patterns. In the basic localized feature selection problem, we design a correlation filter from a single optimal local feature. In the general localized feature selection problem, we design a correlation filter composed of several local features. We show that the discrimination ability of a correlation filter designed form properly selected local features is actually greater than the discrimination ability of a traditional matched filter.
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机译：我们介绍了一种设计相关滤波器的自动方法，用于图案识别，其由选择本地特征（即，参考对象的小部分）组成。选择本地特征以区分参考对象和其他已知对象或模式之间的能力。在基本的本地化功能选择问题中，我们从单个最佳本地功能设计相关滤波器。在一般的本地化特征选择问题中，我们设计了由几个本地功能组成的相关滤波器。 We show that the discrimination ability of a correlation filter designed form properly selected local features is actually greater than the discrimination ability of a traditional matched filter.
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