公开(公告)号：US07983490B1

公开(公告)日：2011-07-19

申请号：US12004329

申请日：2007-12-20

申请人： Thomas Cecil Minter

发明人： Thomas Cecil Minter

IPC分类号： G06K9/62

CPC分类号： G06K9/6284 ,  G06K9/6226 ,  G06K9/6278

摘要： A system and method for classifying input patterns into two classes, a class-of-interest and a class-other, utilizing a method for estimating an optimal Bayes decision boundary for discriminating between the class-of-interest and class-other, when training samples or otherwise, are provided a priori only for the class-of-interest thus eliminates the requirement for any a priori knowledge of the other classes in the data set to be classified, while exploiting the robust and powerful discriminating capability provided by fully supervised Bayes classification approaches. The system and method may be used in applications where class definitions, through training samples or otherwise, are provided a priori only for the classes-of-interest. The distribution of the other-class may be unknown or may have changed. Often one is only interested in one class or a small number of classes.

摘要翻译： 一种用于将输入模式分类为两类，一类兴趣类和另一类的系统和方法，利用一种估计最佳贝叶斯决策边界的方法来区分兴趣类和类别之间，当训练时 样本或其他方式，仅为兴趣类提供，因此消除了对数据集中其他类别的任何先验知识的要求，同时利用由完全监督的贝叶斯提供的强大和强大的辨别能力 分类方法 系统和方法可以用于通过培训样本或其他方式仅为兴趣类提供类别定义的应用程序。 另一类的分配可能是未知的或可能已经改变。 通常只有一个班或少数班的兴趣。

公开(公告)号：US07961956B1

公开(公告)日：2011-06-14

申请号：US12584316

申请日：2009-09-03

申请人： Thomas Cecil Minter

发明人： Thomas Cecil Minter

IPC分类号： G06K9/62

CPC分类号： G06K9/6234 ,  G06K9/6259 ,  G06K9/6269 ,  G06K9/6278 ,  G06K9/6284 ,  G06K9/6286

摘要： This invention relates generally to a system and method for classifying input patterns into two classes, a class-of-interest or a class-other, utilizing an Adaptive Fisher's Linear Discriminant method capable of estimating an optimal Fisher's linear decision boundary for discriminating between the two classes, when training samples are provided a priori only for the class-of-interest. The system and method eliminates the requirement for any a priori knowledge of the other classes in the data set to be classified. The system and method is capable of extracting statistical information corresponding to the “other classes” from the data set to be classified, without recourse to the a priori knowledge normally provided by training samples from the other classes. The system and method can re-optimize (adapt) the decision boundary to provide optimal Fisher's linear discrimination between the two classes in a new data set, using only unlabeled samples from the new data set.

摘要翻译： 本发明一般涉及一种用于将输入模式分类为两类，即兴趣类别或类别的系统和方法，利用自适应Fisher线性判别方法能够估计最佳Fisher's线性判别边界以区分两者 当培训样本仅为兴趣类提供时，才能提供课程。 系统和方法消除了对要分类的数据集中的其他类的任何先验知识的要求。 该系统和方法能够从对待分类的数据集中提取与“其他类别”相对应的统计信息，而不需要通过训练来自其他类的样本通常提供的先验知识。 系统和方法可以重新优化（适应）决策边界，以便在新数据集中仅使用未标记的新数据集中的样本，以提供两类之间的最佳Fisher线性判别。

公开(公告)号：US07961955B1

公开(公告)日：2011-06-14

申请号：US12011518

申请日：2008-01-28

申请人： Thomas Cecil Minter

发明人： Thomas Cecil Minter

IPC分类号： G06K9/62

CPC分类号： G06K9/6247 ,  G06K9/6259 ,  G06K9/6278 ,  G06K9/6284 ,  G06K9/6285

摘要： A system and method for extracting “discriminately informative features” from input patterns which provide accurate discrimination between two classes, a class-of-interest and a class-other, while reducing the number of features under the condition where training samples or otherwise, are provided a priori only for the class-of-interest thus eliminating the requirement for any a priori knowledge of the other classes in the input-data-set while exploiting the potentially robust and powerful feature extraction capability provided by fully supervised feature extraction approaches. The system and method extracts discriminate features by exploiting the ability of the adaptive Bayes classifier to define an optimal Bayes decision boundary between the class-of-interest and class-other using only labeled samples from the class-of-interest and unlabeled samples from the data to be classified. Optimal features are derived from vectors normal to the decision boundary defined by the adaptive Bayes classifier.

摘要翻译： 从输入模式中提取“区分信息特征”的系统和方法，其提供两类之间的准确区分，一类感兴趣类和另一类，同时减少在训练样本或其他方式下的特征数量 仅为兴趣类提供先验，从而消除对输入数据集中的其他类的任何先验知识的要求，同时利用由完全监督的特征提取方法提供的潜在鲁棒强大的特征提取能力。 该系统和方法通过利用自适应贝叶斯分类器的能力来确定兴趣类别和类别之间的最佳贝叶斯决策边界，通过利用来自兴趣类别的标签样本和未标记的样本来提取辨别特征 要分类的数据。 最优特征是从自适应贝叶斯分类器定义的决策边界的向量导出的。

公开(公告)号：US07979363B1

公开(公告)日：2011-07-12

申请号：US12074901

申请日：2008-03-06

申请人： Thomas Cecil Minter

发明人： Thomas Cecil Minter

IPC分类号： G06F15/18

CPC分类号： G06N7/005

摘要： A system and method for estimating the a priori probability of a class-of-interest in an input-data-set and a system and method for evaluating the performance of the adaptive Bayes classifier in classifying unlabeled samples from an input-put-data-set. The adaptive Bayes classifier provides a capability to classify data into two classes, a class-of-interest or a class-other, with minimum classification error in an environment where a priori knowledge, through training samples or otherwise, is only available for a single class, the class-of-interest. This invention provides a method and system for estimating the a priori probability of the class-of-interest in the data set to be classified and evaluating adaptive Bayes classifier performance in classifying data into two classes, a class-of-interest and a class-other, using only labeled training samples, or otherwise, from the class-of-interest and unlabeled samples from the data set to be classified.

摘要翻译： 一种用于估计输入数据集中兴趣类别的先验概率的系统和方法以及用于评估自适应贝叶斯分类器在从输入数据集合中分类未标记样本的性能的系统和方法中， 组。 自适应贝叶斯分类器提供了一种能力，将数据分为两类，即兴趣类别或类别，在具有最小分类误差的环境中，通过培训样本或其他方式，先验知识仅可用于单个 上课，兴趣类。 本发明提供了一种用于估计要分类的数据集中的兴趣类别的先验概率的方法和系统，并且评估自适应贝叶斯分类器性能将数据分类为两类，即兴趣类和类别， 另一方面，仅使用来自要分类的数据集的兴趣类别和未标记样本的标记培训样本，或以其他方式。

公开(公告)号：US07974475B1

公开(公告)日：2011-07-05

申请号：US12583395

申请日：2009-08-20

申请人： Thomas Cecil Minter

发明人： Thomas Cecil Minter

IPC分类号： G06K9/62

CPC分类号： G06K9/6203 ,  G06K9/6278

摘要： This invention relates generally to a system and method for correlating two images for the purpose of identifying a target in an image where templates are provided a priori only for the target. Information on other objects in the image being searched may be unavailable or difficult to obtain. This invention treats the design of target matching-templates and target matched-filters for image correlation as a statistical pattern recognition problem. By minimizing a suitable criterion, a target matching-template or a target matched-filter is estimated which approximates the optimal Bayes discriminant function in a least-squares sense. Both Bayesian image correlation methods identify the target with minimum probability of error while requiring no prior knowledge of other objects in the image being searched. The system and method is adaptive in that it can be re-optimizing (adapted) to recognize the target in a new search image using only information from the new image.

摘要翻译： 本发明一般涉及一种用于将两个图像相关联以用于识别图像中的目标的系统和方法，其中仅为目标提供模板。 关于正在搜索的图像中的其他对象的信息可能不可用或难以获得。 本发明将图像相关的目标匹配模板和目标匹配滤波器的设计视为统计模式识别问题。 通过最小化合适的标准，估计目标匹配模板或目标匹配滤波器，其近似于最小二乘法中的最佳贝叶斯判别函数。 贝叶斯图像相关方法以最小误差概率识别目标，而不需要在正被搜索的图像中的其他对象的先验知识。 系统和方法是自适应的，因为它可以通过仅使用来自新图像的信息来重新优化（适应）以识别新的搜索图像中的目标。