公开(公告)号：US09053392B2

公开(公告)日：2015-06-09

申请号：US14012770

申请日：2013-08-28

Applicant: Adobe Systems Incorporated

Inventor： Jianchao Yang ,  Guang Chen ,  Hailin Jin ,  Jonathan Brandt ,  Elya Shechtman

IPC: G06K9/62 ,  G06K9/68

CPC classification number: G06K9/6267 ,  G06K9/00979 ,  G06K9/4671 ,  G06K9/6219 ,  G06K9/6298 ,  G06K9/6828

Abstract: A hierarchy machine may be configured as a clustering machine that utilizes local feature embedding to organize visual patterns into nodes that each represent one or more visual patterns. These nodes may be arranged as a hierarchy in which a node may have a parent-child relationship with one or more other nodes. The hierarchy machine may implement a node splitting and tree-learning algorithm that includes hard-splitting of nodes and soft-assignment of nodes to perform error-bounded splitting of nodes into clusters. This may enable the hierarchy machine, which may form all or part of a visual pattern recognition system, to perform large-scale visual pattern recognition, such as font recognition or facial recognition, based on a learned error-bounded tree of visual patterns.

Abstract translation: 层次机器可以被配置为利用局部特征嵌入将可视图案组织成每个表示一个或多个视觉图案的节点的聚类机器。 这些节点可以被布置为其中节点可以与一个或多个其他节点具有父子关系的层级。 层次机器可以实现节点分割和树学习算法，其包括节点的硬分割和节点的软分配，以执行节点到分簇的有界限制的分割。 这可以使得可以形成视觉图案识别系统的全部或一部分的层次机器基于学习的有界错误的视觉图案树来执行诸如字体识别或面部识别的大规模视觉模式识别。

公开(公告)号：US09141885B2

公开(公告)日：2015-09-22

申请号：US13953394

申请日：2013-07-29

Applicant: Adobe Systems Incorporated

Inventor： Jianchao Yang ,  Guang Chen ,  Jonathan Brandt ,  Hailin Jin ,  Elya Shechtman ,  Aseem Omprakash Agarwala

IPC: G06K9/00 ,  G06K9/62

CPC classification number: G06K9/627 ,  G06K9/4642

Abstract: A system may be configured as an image recognition machine that utilizes an image feature representation called local feature embedding (LFE). LFE enables generation of a feature vector that captures salient visual properties of an image to address both the fine-grained aspects and the coarse-grained aspects of recognizing a visual pattern depicted in the image. Configured to utilize image feature vectors with LFE, the system may implement a nearest class mean (NCM) classifier, as well as a scalable recognition algorithm with metric learning and max margin template selection. Accordingly, the system may be updated to accommodate new classes with very little added computational cost. This may have the effect of enabling the system to readily handle open-ended image classification problems.

Abstract translation: 系统可以被配置为利用称为局部特征嵌入（LFE）的图像特征表示的图像识别机器。 LFE能够生成捕获图像的显着视觉特性的特征向量，以解决识别图像中描绘的视觉图案的细粒度方面和粗粒度方面。 配置为利用具有LFE的图像特征向量，系统可以实现最近的等级均值（NCM）分类器，以及具有度量学习和最大边距模板选择的可缩放识别算法。 因此，可以更新系统以容纳新类别，而且增加了很少的计算成本。 这可能具有使系统能够容易地处理开放式图像分类问题的效果。

公开(公告)号：US20150030238A1

公开(公告)日：2015-01-29

申请号：US13953394

申请日：2013-07-29

Applicant: Adobe Systems Incorporated

Inventor： Jianchao Yang ,  Guang Chen ,  Jonathan Brandt ,  Hailin Jin ,  Elya Shechtman ,  Aseem Omprakash Agarwala

IPC: G06K9/62

CPC classification number: G06K9/627 ,  G06K9/4642

Abstract: A system may be configured as an image recognition machine that utilizes an image feature representation called local feature embedding (LFE). LFE enables generation of a feature vector that captures salient visual properties of an image to address both the fine-grained aspects and the coarse-grained aspects of recognizing a visual pattern depicted in the image. Configured to utilize image feature vectors with LFE, the system may implement a nearest class mean (NCM) classifier, as well as a scalable recognition algorithm with metric learning and max margin template selection. Accordingly, the system may be updated to accommodate new classes with very little added computational cost. This may have the effect of enabling the system to readily handle open-ended image classification problems.

Abstract translation: 系统可以被配置为利用称为局部特征嵌入（LFE）的图像特征表示的图像识别机器。 LFE能够生成捕获图像的显着视觉特性的特征向量，以解决识别图像中描绘的视觉图案的细粒度方面和粗粒度方面。 配置为利用具有LFE的图像特征向量，系统可以实现最近的等级均值（NCM）分类器，以及具有度量学习和最大边距模板选择的可缩放识别算法。 因此，可以更新系统以容纳新类别，而且增加了很少的计算成本。 这可能具有使系统能够容易地处理开放式图像分类问题的效果。

公开(公告)号：US09524449B2

公开(公告)日：2016-12-20

申请号：US14107191

申请日：2013-12-16

Applicant: Adobe Systems Incorporated

Inventor： Jianchao Yang ,  Guang Chen ,  Hailin Jin ,  Jonathan Brandt ,  Elya Shechtman ,  Aseem Omprakash Agarwala

IPC: G06K9/62 ,  G06K9/68

CPC classification number: G06K9/6267 ,  G06K9/6219 ,  G06K9/6282 ,  G06K9/6807

Abstract: Example systems and methods for classifying visual patterns into a plurality of classes are presented. Using reference visual patterns of known classification, at least one image or visual pattern classifier is generated, which is then employed to classify a plurality of candidate visual patterns of unknown classification. The classification scheme employed may be hierarchical or nonhierarchical. The types of visual patterns may be fonts, human faces, or any other type of visual patterns or images subject to classification.

Abstract translation: 提出了将视觉模式分类为多个类的示例系统和方法。 使用已知分类的参考视觉图案，生成至少一个图像或视觉模式分类器，然后将其用于对未知分类的多个候选视觉图案进行分类。 所使用的分类方案可以是分层的或非分层的。 视觉图案的类型可以是字体，人脸或任何其他类型的可分类的视觉图案或图像。

公开(公告)号：US20150170000A1

公开(公告)日：2015-06-18

申请号：US14107191

申请日：2013-12-16

Applicant: Adobe Systems Incorporated

Inventor： Jianchao Yang ,  Guang Chen ,  Hailin Jin ,  Jonathan Brandt ,  Elya Shechtman ,  Aseem Omprakash Agarwala

IPC: G06K9/62

CPC classification number: G06K9/6267 ,  G06K9/6219 ,  G06K9/6282 ,  G06K9/6807

Abstract: Example systems and methods for classifying visual patterns into a plurality of classes are presented. Using reference visual patterns of known classification, at least one image or visual pattern classifier is generated, which is then employed to classify a plurality of candidate visual patterns of unknown classification. The classification scheme employed may be hierarchical or nonhierarchical. The types of visual patterns may be fonts, human faces, or any other type of visual patterns or images subject to classification.

Abstract translation: 提出了将视觉模式分类为多个类的示例系统和方法。 使用已知分类的参考视觉图案，生成至少一个图像或视觉模式分类器，然后将其用于对未知分类的多个候选视觉图案进行分类。 所使用的分类方案可以是分层的或非分层的。 视觉图案的类型可以是字体，人脸或任何其他类型的可分类的视觉图案或图像。

公开(公告)号：US20150063713A1

公开(公告)日：2015-03-05

申请号：US14012770

申请日：2013-08-28

Applicant: Adobe Systems Incorporated

Inventor： Jianchao Yang ,  Guang Chen ,  Hailin Jin ,  Jonathan Brandt ,  Elya Shechtman

IPC: G06K9/62

CPC classification number: G06K9/6267 ,  G06K9/00979 ,  G06K9/4671 ,  G06K9/6219 ,  G06K9/6298 ,  G06K9/6828

Abstract: A hierarchy machine may be configured as a clustering machine that utilizes local feature embedding to organize visual patterns into nodes that each represent one or more visual patterns. These nodes may be arranged as a hierarchy in which a node may have a parent-child relationship with one or more other nodes. The hierarchy machine may implement a node splitting and tree-learning algorithm that includes hard-splitting of nodes and soft-assignment of nodes to perform error-bounded splitting of nodes into clusters. This may enable the hierarchy machine, which may form all or part of a visual pattern recognition system, to perform large-scale visual pattern recognition, such as font recognition or facial recognition, based on a learned error-bounded tree of visual patterns.

Abstract translation: 层次机器可以被配置为利用局部特征嵌入将可视图案组织成每个表示一个或多个视觉图案的节点的聚类机器。 这些节点可以被布置为其中节点可以与一个或多个其他节点具有父子关系的层级。 层次机器可以实现节点分割和树学习算法，其包括节点的硬分割和节点的软分配，以执行节点到分簇的有界限制的分割。 这可以使得可以形成视觉图案识别系统的全部或一部分的层次机器基于学习的有界错误的视觉图案树来执行诸如字体识别或面部识别的大规模视觉模式识别。