公开(公告)号：US08762390B2

公开(公告)日：2014-06-24

申请号：US13679317

申请日：2012-11-16

Applicant: NEC Laboratories America, Inc.

Inventor： Ming Yang ,  Shaoting Zhang ,  Kai Yu

IPC: G06F7/00 ,  G06F17/30

CPC classification number: G06F17/30256

Abstract: Systems and methods for image retrieval include constructing a plurality of graphs including a first graph for candidate images retrieved based upon holistic features of a query image and a second graph for candidate images retrieved based upon local features of the query image, wherein constructing includes weighting connected images based upon a Jaccard similarity coefficient. The plurality of graphs are fused to provide a fused graph. Candidate images of the fused graph are ranked, using a processor, to provide retrieval results of the query image.

Abstract translation: 用于图像检索的系统和方法包括构建多个图，包括基于查询图像的整体特征检索的候选图像的第一图和用于基于查询图像的局部特征检索的候选图像的第二图，其中构造包括加权连接 基于Jaccard相似系数的图像。 将多个图形融合以提供融合图。 使用处理器对融合图的候选图像进行排序以提供查询图像的检索结果。

公开(公告)号：US20130156327A1

公开(公告)日：2013-06-20

申请号：US13716294

申请日：2012-12-17

Applicant: NEC LABORATORIES AMERICA, INC.

Inventor： Manmohan Chandraker ,  Kai Yu

IPC: G06K9/62

CPC classification number: G06K9/6267 ,  G06T7/579 ,  G06T2207/10016 ,  G06T2207/10024 ,  G06T2207/10152 ,  G06T2207/30244

Abstract: A computer implemented method for determining shape from differential motion with unknown reflectance includes deriving a general relation that relates spatial and temporal image derivatives to bidirectional reflectance distribution function BRDF derivatives, responsive to 3D points and relative camera poses from images and feature tracks of an object in motion under colocated and unknown directional light conditions, employing a rank deficiency in image sequences from the deriving for shape determinations, under predetermined multiple camera and lighting conditions, to eliminate BDRF terms; and recovering a surface depth for determining a shape of the object.

Abstract translation: 用于根据具有未知反射的差分运动来确定形状的计算机实现方法包括导出将空间和时间图像导数与双向反射分布函数BRDF导数相关联的一般关系，其响应于来自图像中的3D点和相对相机姿态的对象的图像和特征轨迹 在预定的多个照相机和照明条件下，在共定位和未知的定向光条件下运动，在来自形状确定的图像序列中使用秩缺陷，以消除BDRF项; 以及恢复用于确定所述物体的形状的表面深度。

公开(公告)号：US20130129202A1

公开(公告)日：2013-05-23

申请号：US13682780

申请日：2012-11-21

Applicant: NEC Laboratories America, Inc.

Inventor： Chang Huang ,  Shenghuo Zhu ,  Kai Yu

IPC: G06K9/62

CPC classification number: G06K9/6256 ,  G06K9/6232

Abstract: Systems and methods for metric learning include iteratively determining feature groups of images based on its derivative norm. Corresponding metrics of the feature groups are learned by gradient descent based on an expected loss. The corresponding metrics are combined to provide an intermediate metric matrix as a sparse representation of the images. A loss function of all metric parameters corresponding to features of the intermediate metric matrix are optimized, using a processor, to learn a final metric matrix. Eigenvalues of the final metric matrix are projected onto a simplex.

Abstract translation: 度量学习的系统和方法包括基于其导数规范迭代确定图像的特征组。 通过基于预期损失的梯度下降来学习特征组的相应度量。 相应的度量被组合以提供作为图像的稀疏表示的中间度量矩阵。 使用处理器来优化对应于中间度量矩阵的特征的所有度量参数的损失函数来学习最终的度量矩阵。 最终公制矩阵的特征值被投影到单纯形上。

公开(公告)号：US08879851B2

公开(公告)日：2014-11-04

申请号：US13716294

申请日：2012-12-17

Applicant: NEC Laboratories America, Inc.

Inventor： Manmohan Chandraker ,  Kai Yu

IPC: G06K9/00 ,  F21S8/10 ,  G06K9/62 ,  G06T7/00

CPC classification number: G06K9/6267 ,  G06T7/579 ,  G06T2207/10016 ,  G06T2207/10024 ,  G06T2207/10152 ,  G06T2207/30244

Abstract: A computer implemented method for determining shape from differential motion with unknown reflectance includes deriving a general relation that relates spatial and temporal image derivatives to bidirectional reflectance distribution function BRDF derivatives, responsive to 3D points and relative camera poses from images and feature tracks of an object in motion under colocated and unknown directional light conditions, employing a rank deficiency in image sequences from the deriving for shape determinations, under predetermined multiple camera and lighting conditions, to eliminate BDRF terms; and recovering a surface depth for determining a shape of the object.

Abstract translation: 用于根据具有未知反射的差分运动来确定形状的计算机实现方法包括导出将空间和时间图像导数与双向反射分布函数BRDF导数相关联的一般关系，其响应于来自图像中的3D点和相对相机姿态的对象的图像和特征轨迹 在预定的多个照相机和照明条件下，在共定位和未知的定向光条件下运动，在来自形状确定的图像序列中使用秩缺陷，以消除BDRF项; 以及恢复用于确定所述物体的形状的表面深度。

公开(公告)号：US08873844B2

公开(公告)日：2014-10-28

申请号：US13682780

申请日：2012-11-21

Applicant: NEC Laboratories America, Inc.

Inventor： Chang Huang ,  Shenghuo Zhu ,  Kai Yu

IPC: G06K9/62

CPC classification number: G06K9/6256 ,  G06K9/6232

Abstract: Systems and methods for metric learning include iteratively determining feature groups of images based on its derivative norm. Corresponding metrics of the feature groups are learned by gradient descent based on an expected loss. The corresponding metrics are combined to provide an intermediate metric matrix as a sparse representation of the images. A loss function of all metric parameters corresponding to features of the intermediate metric matrix are optimized, using a processor, to learn a final metric matrix. Eigenvalues of the final metric matrix are projected onto a simplex.

Abstract translation: 度量学习的系统和方法包括基于其导数规范迭代确定图像的特征组。 通过基于预期损失的梯度下降来学习特征组的相应度量。 相应的度量被组合以提供作为图像的稀疏表示的中间度量矩阵。 使用处理器来优化对应于中间度量矩阵的特征的所有度量参数的损失函数来学习最终的度量矩阵。 最终公制矩阵的特征值被投影到单纯形上。

公开(公告)号：US08761510B2

公开(公告)日：2014-06-24

申请号：US13676494

申请日：2012-11-14

Applicant: NEC Laboratories America, Inc.

Inventor： Olga Russakovsky ,  Yuanqing Lin ,  Kai Yu ,  Fei-Fei Li

IPC: G06K9/00 ,  G06K9/62 ,  G06K9/46

CPC classification number: G06K9/62 ,  G06K9/00624 ,  G06K9/3233 ,  G06K9/46 ,  G06K9/6256

Abstract: A method is provided for classifying an image. The method includes inferring location information of an object of interest in an input representation of the image. The method further includes determining foreground object features and background object features from the input representation of the image. The method additionally includes pooling the foreground object features separately from the background object features using the location information to form a new representation of the image. The new representation is different than the input representation of the image. The method also includes classifying the image based on the new representation of the image.

Abstract translation: 提供了一种用于对图像进行分类的方法。 该方法包括在图像的输入表示中推断感兴趣对象的位置信息。 该方法还包括从图像的输入表示中确定前景对象特征和背景对象特征。 该方法还包括使用位置信息与背景对象特征分开地集合前景对象特征以形成图像的新表示。 新的表示与图像的输入表示不同。 该方法还包括基于图像的新表示对图像进行分类。