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公开(公告)号：US20100076723A1
公开(公告)日：2010-03-25
申请号：US12235927
申请日：2008-09-23
申请人： Wei Zhang , Zhouchen Lin , Xiaoou Tang
发明人： Wei Zhang , Zhouchen Lin , Xiaoou Tang
CPC分类号： G06F17/30598 , G06K9/6234
摘要： Tensor linear Laplacian discrimination for feature extraction is disclosed. One embodiment comprises generating a contextual distance based sample weight and class weight, calculating a within-class scatter using the at least one sample weight and a between-class scatter for multiple classes of data samples in a sample set using the class weight, performing a mode-k matrix unfolding on scatters and generating at least one orthogonal projection matrix.
摘要翻译： 公开了用于特征提取的张量线性拉普拉斯判别。 一个实施例包括生成基于上下文距离的样本权重和类权重，使用所述至少一个样本权重来计算类内散度，以及使用类权重在样本集合中的多类数据样本之间进行类间散射，执行 mode-k矩阵在散射上展开并生成至少一个正交投影矩阵。
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公开(公告)号：US08024152B2
公开(公告)日：2011-09-20
申请号：US12235927
申请日：2008-09-23
申请人： Wei Zhang , Zhouchen Lin , Xiaoou Tang
发明人： Wei Zhang , Zhouchen Lin , Xiaoou Tang
CPC分类号： G06F17/30598 , G06K9/6234
摘要： Tensor linear Laplacian discrimination for feature extraction is disclosed. One embodiment comprises generating a contextual distance based sample weight and class weight, calculating a within-class scatter using the at least one sample weight and a between-class scatter for multiple classes of data samples in a sample set using the class weight, performing a mode-k matrix unfolding on scatters and generating at least one orthogonal projection matrix.
摘要翻译： 公开了用于特征提取的张量线性拉普拉斯判别。 一个实施例包括生成基于上下文距离的样本权重和类权重，使用所述至少一个样本权重来计算类内散度，以及使用类权重在样本集合中的多类数据样本之间进行类间散射，执行 mode-k矩阵在散射上展开并生成至少一个正交投影矩阵。
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公开(公告)号：US20100080450A1
公开(公告)日：2010-04-01
申请号：US12242421
申请日：2008-09-30
申请人： Deli Zhao , Zhouchen Lin , Xiaoou Tang
发明人： Deli Zhao , Zhouchen Lin , Xiaoou Tang
IPC分类号： G06K9/62
CPC分类号： G06K9/6234 , G06K9/6252
摘要： Described is using semi-Riemannian geometry in supervised learning to learn a discriminant subspace for classification, e.g., labeled samples are used to learn the geometry of a semi-Riemannian submanifold. For a given sample, the K nearest classes of that sample are determined, along with the nearest samples that are in other classes, and the nearest samples in that sample's same class. The distances between these samples are computed, and used in computing a metric matrix. The metric matrix is used to compute a projection matrix that corresponds to the discriminant subspace. In online classification, as a new sample is received, it is projected into a feature space by use of the projection matrix and classified accordingly.
摘要翻译： 描述了在监督学习中使用半黎曼几何学习学习用于分类的判别子空间，例如，标记的样本用于学习半黎曼子流形歧管的几何形状。 对于给定的样本，该样本的K个最近类别以及其他类别中最近的样本以及该样本同一类中最近的样本进行确定。 计算这些样本之间的距离，并用于计算度量矩阵。 度量矩阵用于计算与判别子空间对应的投影矩阵。 在线分类中，作为收到的新样本，通过使用投影矩阵将其投影到特征空间中并进行分类。
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公开(公告)号：US20090297046A1
公开(公告)日：2009-12-03
申请号：US12129515
申请日：2008-05-29
申请人： Deli Zhao , Zhouchen Lin , Rong Xiao , Xiaoou Tang
发明人： Deli Zhao , Zhouchen Lin , Rong Xiao , Xiaoou Tang
IPC分类号： G06K9/62
CPC分类号： G06K9/00275 , G06K9/6234
摘要： An exemplary method for extracting discriminant feature of samples includes providing data for samples in a multidimensional space; based on the data, computing local similarities for the samples; mapping the local similarities to weights; based on the mapping, formulating an inter-class scatter matrix and an intra-class scatter matrix; and based on the matrices, maximizing the ratio of inter-class scatter to intra-class scatter for the samples to provide discriminate features of the samples. Such a method may be used for classifying samples, recognizing patterns, or other tasks. Various other methods, devices, system, etc., are also disclosed.
摘要翻译： 用于提取样本的判别特征的示例性方法包括在多维空间中提供样本的数据; 基于数据，计算样本的局部相似度; 将局部相似性映射到权重; 基于映射，制定类间散布矩阵和类内散布矩阵; 并且基于矩阵，最大化样本之间的类间散射与类内散射的比率以提供样本的区别特征。 这种方法可用于分类样本，识别模式或其他任务。 还公开了各种其它方法，装置，系统等。
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公开(公告)号：US08218880B2
公开(公告)日：2012-07-10
申请号：US12129515
申请日：2008-05-29
申请人： Deli Zhao , Zhouchen Lin , Rong Xiao , Xiaoou Tang
发明人： Deli Zhao , Zhouchen Lin , Rong Xiao , Xiaoou Tang
CPC分类号： G06K9/00275 , G06K9/6234
摘要： An exemplary method for extracting discriminant feature of samples includes providing data for samples in a multidimensional space; based on the data, computing local similarities for the samples; mapping the local similarities to weights; based on the mapping, formulating an inter-class scatter matrix and an intra-class scatter matrix; and based on the matrices, maximizing the ratio of inter-class scatter to intra-class scatter for the samples to provide discriminate features of the samples. Such a method may be used for classifying samples, recognizing patterns, or other tasks. Various other methods, devices, system, etc., are also disclosed.
摘要翻译： 用于提取样本的判别特征的示例性方法包括在多维空间中提供样本的数据; 基于数据，计算样本的局部相似度; 将局部相似性映射到权重; 基于映射，制定类间散布矩阵和类内散布矩阵; 并且基于矩阵，最大化样本之间的类间散射与类内散射的比率以提供样本的区别特征。 这种方法可用于分类样本，识别模式或其他任务。 还公开了各种其它方法，装置，系统等。
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公开(公告)号：US07996343B2
公开(公告)日：2011-08-09
申请号：US12242421
申请日：2008-09-30
申请人： Deli Zhao , Zhouchen Lin , Xiaoou Tang
发明人： Deli Zhao , Zhouchen Lin , Xiaoou Tang
IPC分类号： G06F11/00
CPC分类号： G06K9/6234 , G06K9/6252
摘要： Described is using semi-Riemannian geometry in supervised learning to learn a discriminant subspace for classification, e.g., labeled samples are used to learn the geometry of a semi-Riemannian submanifold. For a given sample, the K nearest classes of that sample are determined, along with the nearest samples that are in other classes, and the nearest samples in that sample's same class. The distances between these samples are computed, and used in computing a metric matrix. The metric matrix is used to compute a projection matrix that corresponds to the discriminant subspace. In online classification, as a new sample is received, it is projected into a feature space by use of the projection matrix and classified accordingly.
摘要翻译： 描述了在监督学习中使用半黎曼几何学习学习用于分类的判别子空间，例如，标记的样本用于学习半黎曼子流形歧管的几何形状。 对于给定的样本，该样本的K个最近类别以及其他类别中最近的样本以及该样本同一类中最近的样本进行确定。 计算这些样本之间的距离，并用于计算度量矩阵。 度量矩阵用于计算与判别子空间对应的投影矩阵。 在线分类中，作为收到的新样本，通过使用投影矩阵将其投影到特征空间中并进行分类。
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公开(公告)号：US20090097772A1
公开(公告)日：2009-04-16
申请号：US11871764
申请日：2007-10-12
申请人： Deli Zhao , Zhouchen Lin , Xiaoou Tang
发明人： Deli Zhao , Zhouchen Lin , Xiaoou Tang
IPC分类号： G06K9/40
CPC分类号： G06K9/6248
摘要： Systems and methods perform Laplacian Principal Components Analysis (LPCA). In one implementation, an exemplary system receives multidimensional data and reduces dimensionality of the data by locally optimizing a scatter of each local sample of the data. The optimization includes summing weighted distances between low dimensional representations of the data and a mean. The weights of the distances can be determined by a coding length of each local data sample. The system can globally align the locally optimized weighted scatters of the local samples and provide a global projection matrix. The LPCA improves performance of such applications as face recognition and manifold learning.
摘要翻译： 系统和方法执行拉普拉斯主成分分析（LPCA）。 在一个实现中，示例性系统通过局部优化数据的每个局部采样的散射来接收多维数据并且降低数据的维度。 优化包括对数据的低维表示和平均值之间的加权距离求和。 距离的权重可以通过每个本地数据样本的编码长度来确定。 该系统可以对局部采样的局部优化加权散射进行全局对齐，并提供全局投影矩阵。 LPCA可以改善诸如面部识别和歧管学习等应用的性能。
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公开(公告)号：US08238660B2
公开(公告)日：2012-08-07
申请号：US13100891
申请日：2011-05-04
申请人： Zhouchen Lin , Guangcan Liu , Xiaoou Tang
发明人： Zhouchen Lin , Guangcan Liu , Xiaoou Tang
摘要： This disclosure describes an integrated framework for class-unsupervised object segmentation. The class-unsupervised object segmentation occurs by integrating top-down constraints and bottom-up constraints on object shapes using an algorithm in an integrated manner. The algorithm describes a relationship among object parts and superpixels. This process forms object shapes with object parts and oversegments pixel images into the superpixels, with the algorithm in conjunction with the constraints. This disclosure describes computing a mask map from a hybrid graph, segmenting the image into a foreground object and a background, and displaying the foreground object from the background.
摘要翻译： 本公开描述了用于无人监督的对象分割的集成框架。 通过以集成的方式使用算法将自上而下的约束和自下而上的对象形状约束集成在一起，进行类无监督对象分割。 该算法描述了对象部分和超像素之间的关系。 该过程通过对象部分形成对象形状，并将像素图像监视到超像素中，该算法与约束相结合。 本公开描述了从混合图计算掩模图，将图像分割成前景对象和背景，以及从背景显示前景对象。
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公开(公告)号：US07995841B2
公开(公告)日：2011-08-09
申请号：US11860428
申请日：2007-09-24
申请人： Zhouchen Lin , Guangcan Liu , Xiaoou Tang
发明人： Zhouchen Lin , Guangcan Liu , Xiaoou Tang
摘要： This disclosure describes an integrated framework for class-unsupervised object segmentation. The class-unsupervised object segmentation occurs by integrating top-down constraints and bottom-up constraints on object shapes using an algorithm in an integrated manner. The algorithm describes a relationship among object parts and superpixels. This process forms object shapes with object parts and oversegments pixel images into the superpixels, with the algorithm in conjunction with the constraints. This disclosure describes computing a mask map from a hybrid graph, segmenting the image into a foreground object and a background, and displaying the foreground object from the background.
摘要翻译： 本公开描述了用于无人监督的对象分割的集成框架。 通过以集成的方式使用算法将自上而下的约束和自下而上的对象形状约束集成在一起，进行类无监督对象分割。 该算法描述了对象部分和超像素之间的关系。 该过程通过对象部分形成对象形状，并将像素图像监视到超像素中，该算法与约束相结合。 本公开描述了从混合图计算掩模图，将图像分割成前景对象和背景，以及从背景显示前景对象。
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10.发明申请公开(公告)号：US20100067799A1
公开(公告)日：2010-03-18
申请号：US12212222
申请日：2008-09-17
申请人： Guangcan Liu , Zhouchen Lin , Xiaoou Tang
发明人： Guangcan Liu , Zhouchen Lin , Xiaoou Tang
IPC分类号： G06K9/46
CPC分类号： G06K9/4647
摘要： A “globally invariant Radon feature transform,” or “GIRFT,” generates feature descriptors that are both globally affine invariant and illumination invariant. These feature descriptors effectively handle intra-class variations resulting from geometric transformations and illumination changes to provide robust texture classification. In general, GIRFT considers images globally to extract global features that are less sensitive to large variations of material in local regions. Geometric affine transformation invariance and illumination invariance is achieved by converting original pixel represented images into Radon-pixel images by using a Radon Transform. Canonical projection of the Radon-pixel image into a quotient space is then performed using Radon-pixel pairs to produce affine invariant feature descriptors. Illumination invariance of the resulting feature descriptors is then achieved by defining an illumination invariant distance metric on the feature space of each feature descriptor.
摘要翻译： “全局不变的氡特征变换”或“GIRFT”产生全局仿射不变和照明不变的特征描述符。 这些特征描述符有效地处理由几何变换和照明变化产生的类内变化，以提供鲁棒的纹理分类。 一般来说，GIRFT在全球范围内考虑图像，以提取对本地区域的大量材料较不敏感的全局特征。 通过使用Radon变换将原始像素表示的图像转换为氡像素图像来实现几何仿射变换不变性和照度不变性。 然后使用氡 - 像素对执行氡像素图像到商空间的规范投影，以产生仿射不变特征描述符。 然后通过在每个特征描述符的特征空间上定义照明不变距离度量来实现所得特征描述符的照明不变性。
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