公开(公告)号：US09436893B2

公开(公告)日：2016-09-06

申请号：US14091972

申请日：2013-11-27

申请人： Adobe Systems Incorporated

发明人： Jianchao Yang ,  Zhaowen Wang ,  Zhe Lin ,  Jonathan Brandt

IPC分类号： G06K9/62

CPC分类号： G06K9/6269 ,  G06K9/6235

摘要： A system and method for distributed similarity learning for high-dimensional image features are described. A set of data features is accessed. Subspaces from a space formed by the set of data features are determined using a set of projection matrices. Each subspace has a dimension lower than a dimension of the set of data features. Similarity functions are computed for the subspaces. Each similarity function is based on the dimension of the corresponding subspace. A linear combination of the similarity functions is performed to determine a similarity function for the set of data features.

摘要翻译： 描述了用于高维图像特征的分布式相似性学习的系统和方法。 访问一组数据功能。 使用一组投影矩阵来确定由该组数据特征形成的空间的子空间。 每个子空间的尺寸小于数据特征集合的维度。 为子空间计算相似度函数。 每个相似度函数基于相应子空间的维度。 执行相似度函数的线性组合以确定该组数据特征的相似度函数。

公开(公告)号：US08121357B2

公开(公告)日：2012-02-21

申请号：US12091160

申请日：2006-10-23

申请人： Hitoshi Imaoka

发明人： Hitoshi Imaoka

IPC分类号： G06K9/62

CPC分类号： G06K9/00288 ,  G06K9/6235

摘要： [PROBLEMS] To provide a feature extracting method for quickly extracting a feature while preventing lowering of the identification performance of the kernel judgment analysis, a feature extracting system, and a feature extracting program.[MEANS FOR SOLVING PROBLEMS] Judgment feature extracting device (104) computes an interclass covariance matrix SB and an intraclass covariance matrix SW about a learning face image prepared in advance, determines optimum vectors η, γ which maximizes the ratio of the interclass covariance to the intraclass covariance, derives a conversion formula for converting an inputted frequency feature vector x into a frequency feature vector y in a judgment space, and extracts judgment features of a face image for record and a face image for check by using a restructured conversion formula. Similarity computing device (105) computes the similarity by comparing the judgment features. Check judging device judges whether or not the persons are the same by comparing the similarity with a threshold.

摘要翻译： [问题]提供一种特征提取方法，用于快速提取特征，同时防止降低核心判断分析的识别性能，特征提取系统和特征提取程序。 [解决问题的手段]判断特征提取装置（104）计算关于预先准备的学习面部图像的类间协方差矩阵SB和类内协方差矩阵SW，确定最佳向量＆eegr，γ，其使类间协方差与 类内协方差导出用于将输入的频率特征向量x转换为判断空间中的频率特征向量y的转换公式，并且通过使用重构的转换公式来提取用于记录的面部图像的判断特征和用于检查的面部图像。 相似度计算装置（105）通过比较判断特征来计算相似度。 检查判断装置通过将相似度与阈值进行比较来判断人是否相同。

公开(公告)号：US07856123B2

公开(公告)日：2010-12-21

申请号：US11628321

申请日：2004-06-04

申请人： Muriel Visani ,  Christophe Garcia ,  Christophe Laurent

发明人： Muriel Visani ,  Christophe Garcia ,  Christophe Laurent

IPC分类号： G06K9/00

CPC分类号： G06K9/6235 ,  G06K9/00275

摘要： The invention relates to a method for recognizing faces in digital images consisting in providing for a knowledge base which contains face images and is hierarchical into several classes each of which comprises different images of the same person. The invention relates to pre-processing said knowledge base in such a way that a minimization of variance in each class and a maximization of variance between different classes are simultaneously obtainable, thereby making it possible to form a vectorial base comprising the discriminant component of said knowledge base. The comparison of a recognizable face with a pre-processed reference face such as the knowledge base and an eventual reconstruction of a recognized face are also disclosed.

摘要翻译： 本发明涉及一种用于识别数字图像中的面部的方法，包括提供包含面部图像的知识库，并且被分层成几个类别，每个类别包括同一人的不同图像。 本发明涉及以这样的方式预处理所述知识库，使得可以同时获得每个类中的方差最小化和不同类之间的最大化方差，由此可以形成包括所述知识的判别分量的矢量基 基础。 还公开了可识别面部与预处理参考面（例如知识库）和识别面部的最终重建的比较。

公开(公告)号：US20100158396A1

公开(公告)日：2010-06-24

申请号：US12344018

申请日：2008-12-24

申请人： Meng Wang ,  Xian-Sheng Hua

发明人： Meng Wang ,  Xian-Sheng Hua

IPC分类号： G06K9/62

CPC分类号： G06K9/6235 ,  G06K9/6215

摘要： This disclosure describes various exemplary systems, computer program products, and methods for feature distance metric learning with feature decomposition (DMLFD). The disclosure describes decomposing a high-dimensional feature space into one or more low-dimensional feature spaces according to minimum dependence. Furthermore, the disclosure describes how the sub-metrics are constructed and combined to form a global metric.

摘要翻译： 本公开描述了具有特征分解（DMLFD）的各种示例性系统，计算机程序产品和用于特征距离度量学习的方法。 本公开描述了根据最小依赖性将高维特征空间分解为一个或多个低维特征空间。 此外，本公开描述如何构建和组合子度量以形成全局度量。

公开(公告)号：US20090123077A1

公开(公告)日：2009-05-14

申请号：US12091160

申请日：2006-10-23

申请人： Hitoshi Imaoka

发明人： Hitoshi Imaoka

IPC分类号： G06K9/56

CPC分类号： G06K9/00288 ,  G06K9/6235

摘要： [PROBLEMS] To provide a feature extracting method for quickly extracting a feature while preventing lowering of the identification performance of the kernel judgment analysis, a feature extracting system, and a feature extracting program. [MEANS FOR SOLVING PROBLEMS] Judgment feature extracting device (104) computes an interclass covariance matrix SB and an intraclass covariance matrix SW about a learning face image prepared in advance, determines optimum vectors η, γ which maximizes the ratio of the interclass covariance to the intraclass covariance, derives a conversion formula for converting an inputted frequency feature vector x into a frequency feature vector y in a judgment space, and extracts judgment features of a face image for record and a face image for check by using a restructured conversion formula. Similarity computing device (105) computes the similarity by comparing the judgment features. Check judging device judges whether or not the persons are the same by comparing the similarity with a threshold.

摘要翻译： [问题]提供一种特征提取方法，用于快速提取特征，同时防止降低核心判断分析的识别性能，特征提取系统和特征提取程序。 [解决问题的手段]判断特征提取装置（104）计算关于预先准备的学习面部图像的类间协方差矩阵SB和类内协方差矩阵SW，确定最佳向量eta，使最大化跨类协方差与 类内协方差导出用于将输入的频率特征向量x转换为判断空间中的频率特征向量y的转换公式，并且通过使用重构的转换公式来提取用于记录的面部图像的判断特征和用于检查的面部图像。 相似度计算装置（105）通过比较判断特征来计算相似度。 检查判断装置通过将相似度与阈值进行比较来判断人是否相同。

公开(公告)号：US20240068936A1

公开(公告)日：2024-02-29

申请号：US17962602

申请日：2022-10-10

申请人： NCS Testing Technology CO., LTD.

发明人： Jia Liu ,  Feipeng Cui ,  Xiaopeng Li ,  Ling Liu ,  Hongwei Shi ,  Feifei Guo ,  Ying Zhao ,  Xuejing Shen ,  Haizhou Wang

IPC分类号： G01N21/31 ,  G06K9/00 ,  G06K9/62 ,  G06N3/12

CPC分类号： G01N21/3103 ,  G06K9/0051 ,  G06K9/6235 ,  G06N3/126 ,  G06K2009/6237

摘要： An adaptive characteristic spectral line screening method and system based on atomic emission spectrum are provided, the method includes: using a set characteristic screening optimization method to perform a plurality of optimization rounds of characteristic screening, obtaining an initialized spectral dataset of each round of the characteristic screening and initialized characteristic population genes; obtaining an optimal characteristic population gene of each round by a set analysis method, a fitness function, and an iteration of a genetic algorithm; obtaining an optimized characteristic spectral information set when the plurality of optimization rounds reach set optimization rounds; performing combination statistics and discriminant analyses on the optimized characteristic spectral information set to complete an adaptive characteristic spectral line screening. The disclosure can efficiently and automatically screen out the characteristic spectral lines that meet the analysis requirements in the complex atomic emission spectrum, thus ensuring the effectiveness and accuracy of screening the characteristic spectral lines.

公开(公告)号：US20230244991A1

公开(公告)日：2023-08-03

申请号：US17896281

申请日：2022-08-26

申请人： Databricks, Inc.

发明人： Benjamin Thomas Wilson ,  Corey Zumar

IPC分类号： G06N20/00 ,  G06K9/62

CPC分类号： G06N20/00 ,  G06K9/6227 ,  G06K9/6235 ,  G06K2009/6237

摘要： The present application discloses a method, system, and computer system for building a model associated with a dataset. The method includes receiving a data set, the dataset comprising a plurality of keys and a plurality of key-value relationships, determining a plurality of models to build based at least in part on the dataset, wherein determining the plurality of models to build comprises using the dataset format information to identify the plurality of models, building the plurality of models, and optimizing at least one of the plurality of models.

公开(公告)号：US09946959B2

公开(公告)日：2018-04-17

申请号：US15307026

申请日：2014-04-30

申请人： Hewlett Packard Enterprise Development LP

发明人： Ming C. Hao ,  Wei-Nchih Lee ,  Alexander Jaeger ,  Nelson L. Chang ,  Daniel Keim

IPC分类号： G06K9/00 ,  G06K9/62

CPC分类号： G06K9/6224 ,  G06K9/00 ,  G06K9/6215 ,  G06K9/6218 ,  G06K9/6235 ,  G06K2009/6236

摘要： In an example, high-dimensional data is projected to a multi-dimensional space to differentiate clusters of the high-dimensional data. A user selection of at least two of the clusters may be received and a plurality of dissimilar dimensions may be extracted from the at least two clusters. In addition, a user selected of a dissimilar dimension from the plurality of extracted dissimilar dimensions may be received. In response to receipt of the user selection of the dissimilar dimension from the plurality of dissimilar dimensions, a plurality of correlated dimensions to the dissimilar dimension may be determined. In addition, the plurality of dissimilar dimensions and the plurality of correlated dimensions may be displayed.

公开(公告)号：US20140089236A1

公开(公告)日：2014-03-27

申请号：US13733407

申请日：2013-01-03

申请人： ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE

发明人： Yong Jin LEE ,  So Hee PARK ,  Jong Gook KO ,  Ki Young MOON ,  Jang Hee YOO

IPC分类号： G06N99/00

CPC分类号： G06N20/00 ,  G06K9/6231 ,  G06K9/6235 ,  G06K9/6256 ,  G06K9/6282 ,  G06K2009/6236

摘要： Disclosed is a learning method using extracted data features for simplifying a learning process or improving accuracy of estimation. The learning method includes dividing input learning data into two groups based on a predetermined reference, extracting data features for distinguishing the two divided groups, and performing learning using the extracted data features.

摘要翻译： 公开了一种使用提取的数据特征来简化学习过程或提高估计精度的学习方法。 该学习方法包括基于预定参考将输入学习数据分成两组，提取用于区分两个分组的数据特征，以及使用所提取的数据特征进行学习。

公开(公告)号：US07734087B2

公开(公告)日：2010-06-08

申请号：US11002082

申请日：2004-12-03

申请人： Wonjun Hwang ,  Taekyun Kim

发明人： Wonjun Hwang ,  Taekyun Kim

IPC分类号： G06K9/62 ,  G06K9/00 ,  G06K9/50 ,  G06K9/56

CPC分类号： G06K9/6235 ,  G06K9/00275

摘要： A face recognition apparatus and method using Principal Component Analysis (PCA) learning per subgroup, the face recognition apparatus includes: a learning unit which performs Principal Component Analysis (PCA) learning on each of a plurality of subgroups constituting a training data set, and then performs Linear Discriminant Analysis (LDA) learning on the training data set, thereby generating a PCA-based LDA (PCLDA) basis vector set of each subgroup; a feature vector extraction unit which projects a PCLDA basis vector set of each subgroup to an input image and extracts a feature vector set of the input image with respect to each subgroup; a feature vector storing unit which projects a PCLDA basis vector set of each subgroup to each of a plurality of face images to be registered, thereby generating a feature vector set of each registered image with respect to each subgroup, and storing the feature vector set in a database; and a similarity calculation unit which calculates a similarity between the input image and each registered image.

摘要翻译： 一种面部识别装置和方法，使用每个子组的主成分分析（PCA）学习，面部识别装置包括：学习单元，对构成训练数据集的多个子组中的每一个进行主成分分析（PCA）学习，然后 对训练数据集进行线性判别分析（LDA）学习，从而生成每个子组的基于PCA的LDA（PCLDA）基向量集; 特征向量提取单元，其将每个子组的PCLDA基矢量集投影到输入图像，并且针对每个子组提取输入图像的特征向量集合; 特征矢量存储单元，其将每个子组的PCLDA基矢量集投影到要登记的多个面部图像中的每一个，从而针对每个子组生成每个注册图像的特征向量集合，并将设置在 数据库 以及相似度计算单元，其计算输入图像和每个注册图像之间的相似度。