Linear Laplacian Discrimination for Feature Extraction
    1.
    发明申请
    Linear Laplacian Discrimination for Feature Extraction 有权
    线性拉普拉斯算子特征提取

    公开(公告)号:US20090297046A1

    公开(公告)日:2009-12-03

    申请号:US12129515

    申请日:2008-05-29

    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。

    Linear laplacian discrimination for feature extraction
    2.
    发明授权
    Linear laplacian discrimination for feature extraction 有权
    特征提取的线性拉普拉斯判别

    公开(公告)号:US08218880B2

    公开(公告)日:2012-07-10

    申请号:US12129515

    申请日:2008-05-29

    IPC分类号: G06K9/62 G06K9/00 G06K9/46

    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。

    Interactive photo annotation based on face clustering
    3.
    发明授权
    Interactive photo annotation based on face clustering 有权
    基于面部聚类的交互式照片注释

    公开(公告)号:US08189880B2

    公开(公告)日:2012-05-29

    申请号:US11754933

    申请日:2007-05-29

    IPC分类号: G06K9/00 G06K9/62

    CPC分类号: G06F17/30259 G06F17/3028

    摘要: An interactive photo annotation method uses clustering based on facial similarities to improve annotation experience. The method uses a face recognition algorithm to extract facial features of a photo album and cluster the photos into multiple face groups based on facial similarity. The method annotates a face group collectively using annotations, such as name identifiers, in one operation. The method further allows merging and splitting of face groups. Special graphical user interfaces, such as displays in a group view area and a thumbnail area and drag-and-drop features, are used to further improve the annotation experience.

    摘要翻译: 交互式照片注释方法使用基于面部相似度的聚类来改进注释体验。 该方法使用面部识别算法提取相册的面部特征,并根据面部相似度将照片聚类成多个面部组。 该方法在一个操作中使用注释(例如名称标识符)集体使用面部组。 该方法还允许面组的合并和分割。 使用特殊的图形用户界面,例如组视图区域中的显示和缩略图区域以及拖放功能,以进一步改进注释体验。

    Face annotation framework with partial clustering and interactive labeling
    4.
    发明授权
    Face annotation framework with partial clustering and interactive labeling 有权
    面部注释框架,具有部分聚类和交互式标签

    公开(公告)号:US08014572B2

    公开(公告)日:2011-09-06

    申请号:US11760641

    申请日:2007-06-08

    IPC分类号: G06K9/00

    摘要: Systems and methods are described for a face annotation framework with partial clustering and interactive labeling. In one implementation, an exemplary system automatically groups some images of a collection of images into clusters, each cluster mainly including images that contain a person's face associated with that cluster. After an initial user-labeling of each cluster with the person's name or other label, in which the user may also delete/label images that do not belong in the cluster, the system iteratively proposes subsequent clusters for the user to label, proposing clusters of images that when labeled, produce a maximum information gain at each iteration and minimize the total number of user interactions for labeling the entire collection of images.

    摘要翻译: 描述了具有部分聚类和交互式标签的面部注释框架的系统和方法。 在一个实现中,示例性系统自动地将图像集合的一些图像分组成群集,每个群集主要包括包含与该群集相关联的人脸的图像。 在用户的姓名或其他标签对每个集群进行初始用户标签之后,用户还可以在其中删除/标记不属于集群的图像,系统迭代地提出用于用户标签的后续集群,提出集群 标记后的图像在每次迭代时产生最大的信息增益,并最大限度地减少用户标记整个图像集合的总体交互次数。

    Robust online face tracking
    5.
    发明申请
    Robust online face tracking 有权
    强大的在线人脸跟踪

    公开(公告)号:US20070098218A1

    公开(公告)日:2007-05-03

    申请号:US11265773

    申请日:2005-11-02

    IPC分类号: G06K9/00

    摘要: Systems and methods are described for robust online face tracking. In one implementation, a system derives multiple resolutions of each video frame of a video sequence portraying movement of a visual object. The system tracks movement of the visual object in a low resolution as input for tracking the visual object in a higher resolution. The system can greatly reduce jitter while maintaining an ability to reliably track fast-moving visual objects.

    摘要翻译: 描述了用于强大的在线人脸跟踪的系统和方法。 在一个实现中,系统导出描绘视觉对象的移动的视频序列的每个视频帧的多个分辨率。 系统以低分辨率跟踪可视对象的移动,作为用于以更高分辨率跟踪视觉对象的输入。 该系统可以大大减少抖动,同时保持可靠地跟踪快速移动的视觉对象的能力。

    Robust online face tracking
    6.
    发明授权
    Robust online face tracking 有权
    强大的在线人脸跟踪

    公开(公告)号:US08098885B2

    公开(公告)日:2012-01-17

    申请号:US11265773

    申请日:2005-11-02

    IPC分类号: G06K9/00

    摘要: Systems and methods are described for robust online face tracking. In one implementation, a system derives multiple resolutions of each video frame of a video sequence portraying movement of a visual object. The system tracks movement of the visual object in a low resolution as input for tracking the visual object in a higher resolution. The system can greatly reduce jitter while maintaining an ability to reliably track fast-moving visual objects.

    摘要翻译: 描述了用于强大的在线人脸跟踪的系统和方法。 在一个实现中,系统导出描绘视觉对象的移动的视频序列的每个视频帧的多个分辨率。 系统以低分辨率跟踪可视对象的移动,作为用于以更高分辨率跟踪视觉对象的输入。 该系统可以大大减少抖动,同时保持可靠地跟踪快速移动的视觉对象的能力。

    Object detection and recognition with bayesian boosting
    7.
    发明授权
    Object detection and recognition with bayesian boosting 有权
    贝叶斯提升对象检测和识别

    公开(公告)号:US07949621B2

    公开(公告)日:2011-05-24

    申请号:US11871899

    申请日:2007-10-12

    申请人: Rong Xiao Xiaoou Tang

    发明人: Rong Xiao Xiaoou Tang

    IPC分类号: G06F15/18

    CPC分类号: G06N7/005 G06K9/6256

    摘要: An efficient, effective and at times superior object detection and/or recognition (ODR) function may be built from a set of Bayesian stumps. Bayesian stumps may be constructed for each feature and object class, and the ODR function may be constructed from the subset of Bayesian stumps that minimize Bayesian error for a particular object class. That is, Bayesian error may be utilized as a feature selection measure for the ODR function. Furthermore, Bayesian stumps may be efficiently implemented as lookup tables with entries corresponding to unequal intervals of feature histograms. Interval widths and entry values may be determined so as to minimize Bayesian error, yielding Bayesian stumps that are optimal in this respect.

    摘要翻译: 可以从一组贝叶斯树桩构建一个有效,有效且有时优越的物体检测和/或识别(ODR)功能。 可以为每个特征和对象类构造贝叶斯树桩,并且可以从贝叶斯树桩的子集构建ODR功能,以使特定对象类的贝叶斯误差最小化。 也就是说,贝叶斯误差可以用作ODR功能的特征选择测量。 此外,贝叶斯树桩可以被有效地实现为具有对应于特征直方图的不等间隔的条目的查找表。 间隔宽度和入口值可以被确定为使贝叶斯误差最小化,从而产生在这方面是最佳的贝叶斯树桩。

    Joint boosting feature selection for robust face recognition
    8.
    发明授权
    Joint boosting feature selection for robust face recognition 有权
    联合提升功能选择,强大的人脸识别

    公开(公告)号:US07668346B2

    公开(公告)日:2010-02-23

    申请号:US11277098

    申请日:2006-03-21

    申请人: Rong Xiao Xiaoou Tang

    发明人: Rong Xiao Xiaoou Tang

    IPC分类号: G06K9/00

    CPC分类号: G06K9/6256 G06K9/00281

    摘要: Methods and systems are provided for selecting features that will be used to recognize faces. Three-dimensional models are used to synthesize a database of virtual face images. The virtual face images cover wide appearance variations, different poses, different lighting conditions and expression changes. A joint boosting algorithm is used to identify discriminative features by selecting features from the plurality of virtual images such that the identified discriminative features are independent of the other images included in the database.

    摘要翻译: 提供了用于选择将用于识别面部的特征的方法和系统。 三维模型用于合成虚拟脸部图像的数据库。 虚拟脸部图像涵盖宽的外观变化,不同的姿势,不同的照明条件和表情变化。 联合增强算法用于通过从多个虚拟图像中选择特征来识别识别特征,使得所识别的鉴别特征与包括在数据库中的其它图像无关。

    Face Annotation Framework With Partial Clustering And Interactive Labeling
    9.
    发明申请
    Face Annotation Framework With Partial Clustering And Interactive Labeling 有权
    面部注释框架与部分聚类和交互式标签

    公开(公告)号:US20080304755A1

    公开(公告)日:2008-12-11

    申请号:US11760641

    申请日:2007-06-08

    IPC分类号: G06K9/62 G06K9/00

    摘要: Systems and methods are described for a face annotation framework with partial clustering and interactive labeling. In one implementation, an exemplary system automatically groups some images of a collection of images into clusters, each cluster mainly including images that contain a person's face associated with that cluster. After an initial user-labeling of each cluster with the person's name or other label, in which the user may also delete/label images that do not belong in the cluster, the system iteratively proposes subsequent clusters for the user to label, proposing clusters of images that when labeled, produce a maximum information gain at each iteration and minimize the total number of user interactions for labeling the entire collection of images.

    摘要翻译: 描述了具有部分聚类和交互式标签的面部注释框架的系统和方法。 在一个实现中,示例性系统自动地将图像集合的一些图像分组成群集,每个群集主要包括包含与该群集相关联的人脸的图像。 在用户的姓名或其他标签对每个集群进行初始用户标签之后,用户还可以在其中删除/标记不属于集群的图像,系统迭代地提出用于用户标签的后续集群,提出集群 标记后的图像在每次迭代时产生最大的信息增益,并最大限度地减少用户标记整个图像集合的总体交互次数。

    OBJECT DETECTION AND RECOGNITION WITH BAYESIAN BOOSTING
    10.
    发明申请
    OBJECT DETECTION AND RECOGNITION WITH BAYESIAN BOOSTING 有权
    用BAYESIAN BOOSTING进行对象检测和识别

    公开(公告)号:US20090099990A1

    公开(公告)日:2009-04-16

    申请号:US11871899

    申请日:2007-10-12

    申请人: Rong Xiao Xiaoou Tang

    发明人: Rong Xiao Xiaoou Tang

    IPC分类号: G06N5/02

    CPC分类号: G06N7/005 G06K9/6256

    摘要: An efficient, effective and at times superior object detection and/or recognition (ODR) function may be built from a set of Bayesian stumps. Bayesian stumps may be constructed for each feature and object class, and the ODR function may be constructed from the subset of Bayesian stumps that minimize Bayesian error for a particular object class. That is, Bayesian error may be utilized as a feature selection measure for the ODR function. Furthermore, Bayesian stumps may be efficiently implemented as lookup tables with entries corresponding to unequal intervals of feature histograms. Interval widths and entry values may be determined so as to minimize Bayesian error, yielding Bayesian stumps that are optimal in this respect.

    摘要翻译: 可以从一组贝叶斯树桩构建一个有效,有效且有时优越的物体检测和/或识别(ODR)功能。 可以为每个特征和对象类构造贝叶斯树桩,并且可以从贝叶斯树桩的子集构建ODR功能,以使特定对象类的贝叶斯误差最小化。 也就是说,贝叶斯误差可以用作ODR功能的特征选择测量。 此外,贝叶斯树桩可以被有效地实现为具有对应于特征直方图的不等间隔的条目的查找表。 间隔宽度和入口值可以被确定为使贝叶斯误差最小化,从而产生在这方面是最佳的贝叶斯树桩。