-
公开(公告)号：US20150170001A1
公开(公告)日：2015-06-18
申请号：US14027512
申请日：2013-09-16
Applicant: Google Inc.
Inventor： Andrew Rabinovich , Hartwig Adam
CPC classification number: G06K9/66 , G06K9/6256 , G06K9/6267 , G06K2209/17
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting training images. One of the methods includes determining, for each of a plurality of labels that each designate a respective food class of a plurality of food classes, a respective measure of importance. A respective sample size is determined for the label based on the respective measure of importance of the label. A number of training images are selected for each respective label according to the determined sample size for the label. A predictive model is trained using the selected training images as training data.
Abstract translation: 方法，系统和装置，包括在计算机存储介质上编码的计算机程序，用于选择训练图像。 其中一种方法包括为每个指定多个食品类别的相应食品类别的多个标签中的每一个确定相应的重要度量。 基于标签的重要性的相应测量，确定标签的相应样本大小。 根据标签的确定的样本大小，为每个各个标签选择多个训练图像。 使用所选择的训练图像作为训练数据训练预测模型。
-
公开(公告)号：US20150169998A1
公开(公告)日：2015-06-18
申请号：US14204407
申请日：2014-03-11
Applicant: Google Inc.
Inventor： Andrew Rabinovich , Dragomir Anguelov
CPC classification number: G06K9/6218 , G06K9/00677 , G06K9/6219 , G06K9/6267 , G06K9/66
Abstract: A hierarchy of clusters is determined, where each leave of the hierarchy corresponds to one of the images in a group, and each cluster in the hierarchy identifies images in the group that are deemed similar to one another. The hierarchy identifies a similarity between each of the plurality of clusters.
Abstract translation: 确定集群的层次结构，其中层次结构的每个离开对应于组中的一个图像，并且层次中的每个集合标识组中认为彼此相似的图像。 该层次结构标识多个聚类中的每一个之间的相似性。
-
公开(公告)号：US20150169978A1
公开(公告)日：2015-06-18
申请号：US14254242
申请日：2014-04-16
Applicant: Google Inc.
Inventor： Anand Pillai , Andrew Rabinovich
CPC classification number: G06K9/46 , G06K9/00288 , G06K9/6218 , G06K9/622
Abstract: Methods and systems for selecting a representative image of an entity are disclosed. According to one embodiment, a computer-implemented method for selecting a representative image of an entity is disclosed. The method includes: accessing a collection of images of the entity; clustering, based on similarity of one or more similarity features, images from the collection to form a plurality of similarity clusters; and selecting the representative image from one of said similarity clusters. Further, based on cluster size of said similarity clusters popular clusters can be determined, and the selection of the representative image can be from the popular clusters. In addition, the method can further include assigning a headshot score based upon a portion of the respective image covered by the entity to respective images in said popular clusters, and further selecting the representative image based upon the headshot score.
Abstract translation: 公开了用于选择实体的代表性图像的方法和系统。 根据一个实施例，公开了一种用于选择实体的代表图像的计算机实现的方法。 该方法包括：访问该实体的图像集合; 基于一个或多个相似性特征的相似性，聚集来自所述集合的图像以形成多个相似性聚类; 以及从所述相似性集群之一选择所述代表图像。 此外，基于所述相似度簇的簇大小，可以确定流行的簇，并且可以从流行簇中选择代表图像。 此外，该方法可以进一步包括基于由该实体覆盖的各个图像的一部分将所述流行集群中的各个图像分配头像分数，以及基于所述头像分数进一步选择所述代表图像。
-
公开(公告)号：US09230194B2
公开(公告)日：2016-01-05
申请号：US14027512
申请日：2013-09-16
Applicant: Google Inc.
Inventor： Andrew Rabinovich , Hartwig Adam
CPC classification number: G06K9/66 , G06K9/6256 , G06K9/6267 , G06K2209/17
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting training images. One of the methods includes determining, for each of a plurality of labels that each designate a respective food class of a plurality of food classes, a respective measure of importance. A respective sample size is determined for the label based on the respective measure of importance of the label. A number of training images are selected for each respective label according to the determined sample size for the label. A predictive model is trained using the selected training images as training data.
Abstract translation: 方法，系统和装置，包括在计算机存储介质上编码的计算机程序，用于选择训练图像。 其中一种方法包括为每个指定多个食品类别的相应食品类别的多个标签中的每一个确定相应的重要度量。 基于标签的重要性的相应测量，确定标签的相应样本大小。 根据标签的确定的样本大小，为每个各个标签选择多个训练图像。 使用所选择的训练图像作为训练数据训练预测模型。
-
公开(公告)号：US09208177B2
公开(公告)日：2015-12-08
申请号：US14185392
申请日：2014-02-20
Applicant: Google Inc.
Inventor： David Petrou , Andrew Rabinovich , Hartwig Adam
CPC classification number: G06F17/30268 , G06F17/30256 , G06F17/30277 , G06F17/3053 , G06F17/30867 , G06K9/00288 , G06K9/00677
Abstract: A facial recognition search system identifies one or more likely names (or other personal identifiers) corresponding to the facial image(s) in a query as follows. After receiving the visual query with one or more facial images, the system identifies images that potentially match the respective facial image in accordance with visual similarity criteria. Then one or more persons associated with the potential images are identified. For each identified person, person-specific data comprising metrics of social connectivity to the requester are retrieved from a plurality of applications such as communications applications, social networking applications, calendar applications, and collaborative applications. An ordered list of persons is then generated by ranking the identified persons in accordance with at least metrics of visual similarity between the respective facial image and the potential image matches and with the social connection metrics. Finally, at least one person identifier from the list is sent to the requester.
Abstract translation: 面部识别搜索系统如下所述识别与查询中的面部图像相对应的一个或多个可能的名称（或其他个人标识符）。 在接收到具有一个或多个面部图像的视觉查询之后，系统根据视觉相似性标准识别潜在地匹配相应面部图像的图像。 然后识别与潜在图像相关联的一个或多个人。 对于每个已识别的人，从诸如通信应用，社交网络应用，日历应用和协作应用的多个应用中检索包括对请求者的社交连接性度量的个人特定数据。 然后通过根据相应的面部图像和潜在图像匹配之间的视觉相似性的度量以及与社会连接度量进行评估来对所识别的人进行排序来生成人的有序列表。 最后，从列表中至少有一个人标识符被发送到请求者。
-
Patent Agency Ranking
6.发明申请GEOMETRY-PRESERVING VISUAL PHRASES FOR IMAGE CLASSIFICATION USING LOCAL-DESCRIPTOR-LEVEL WEIGHTS 审中-公开使用本地描述层级权重进行图像分类的几何保留视觉图公开(公告)号：US20150169993A1
公开(公告)日：2015-06-18
申请号：US13632654
申请日：2012-10-01
Applicant: Google Inc.
Inventor： Andrew Rabinovich , Alexander Toshkov Toshev
CPC classification number: G06F16/51 , G06F16/583 , G06K9/6205 , G06K9/6211
Abstract: Implementations relate to techniques for classifying images. Some techniques utilize weights associated with local descriptors to classify images. Some techniques utilize visual phrase matching to classify images. The resulting image classifications can be used in part to assist in internet searches.
Abstract translation: 实现与分类图像的技术有关。 一些技术利用与局部描述符相关联的权重来对图像进行分类。 一些技术利用视觉短语匹配来对图像进行分类。 所得到的图像分类可以部分地用于协助互联网搜索。
-
公开(公告)号：US09367756B2
公开(公告)日：2016-06-14
申请号：US14254242
申请日：2014-04-16
Applicant: Google Inc.
Inventor： Anand Pillai , Andrew Rabinovich
CPC classification number: G06K9/46 , G06K9/00288 , G06K9/6218 , G06K9/622
Abstract: Methods and systems for selecting a representative image of an entity are disclosed. According to one embodiment, a computer-implemented method for selecting a representative image of an entity is disclosed. The method includes: accessing a collection of images of the entity; clustering, based on similarity of one or more similarity features, images from the collection to form a plurality of similarity clusters; and selecting the representative image from one of said similarity clusters. Further, based on cluster size of said similarity clusters popular clusters can be determined, and the selection of the representative image can be from the popular clusters. In addition, the method can further include assigning a headshot score based upon a portion of the respective image covered by the entity to respective images in said popular clusters, and further selecting the representative image based upon the headshot score.
Abstract translation: 公开了用于选择实体的代表性图像的方法和系统。 根据一个实施例，公开了一种用于选择实体的代表性图像的计算机实现的方法。 该方法包括：访问该实体的图像集合; 基于一个或多个相似性特征的相似性，聚集来自所述集合的图像以形成多个相似性聚类; 以及从所述相似性集群之一选择所述代表图像。 此外，基于所述相似度簇的簇大小，可以确定流行簇，并且可以从流行簇中选择代表图像。 此外，该方法可以进一步包括基于由该实体覆盖的各个图像的一部分将所述流行集群中的各个图像分配头像分数，以及基于所述头像分数进一步选择所述代表图像。
-
公开(公告)号：US20160055182A1
公开(公告)日：2016-02-25
申请号：US14929958
申请日：2015-11-02
Applicant: Google Inc.
Inventor： David Petrou , Andrew Rabinovich , Hartwig Adam
CPC classification number: G06F17/30268 , G06F17/30256 , G06F17/30277 , G06F17/3053 , G06F17/30867 , G06K9/00288 , G06K9/00677
Abstract: A facial recognition search system identifies one or more likely names (or other personal identifiers) corresponding to the facial image(s) in a query as follows. After receiving the visual query with one or more facial images, the system identifies images that potentially match the respective facial image in accordance with visual similarity criteria. Then one or more persons associated with the potential images are identified. For each identified person, person-specific data comprising metrics of social connectivity to the requester are retrieved from a plurality of applications such as communications applications, social networking applications, calendar applications, and collaborative applications. An ordered list of persons is then generated by ranking the identified persons in accordance with at least metrics of visual similarity between the respective facial image and the potential image matches and with the social connection metrics. Finally, at least one person identifier from the list is sent to the requester.
Abstract translation: 面部识别搜索系统如下所述识别与查询中的面部图像相对应的一个或多个可能的名称（或其他个人标识符）。 在接收到具有一个或多个面部图像的视觉查询之后，系统根据视觉相似性标准识别潜在地匹配相应面部图像的图像。 然后识别与潜在图像相关联的一个或多个人。 对于每个已识别的人，从诸如通信应用，社交网络应用，日历应用和协作应用的多个应用中检索包括对请求者的社交连接性度量的个人特定数据。 然后通过根据相应的面部图像和潜在图像匹配之间的视觉相似性的度量以及与社会连接度量进行评估来对所识别的人进行排序来生成人的有序列表。 最后，从列表中至少有一个人标识符被发送到请求者。
-
公开(公告)号：US09177226B2
公开(公告)日：2015-11-03
申请号：US14204407
申请日：2014-03-11
Applicant: Google Inc.
Inventor： Andrew Rabinovich , Dragomir Anguelov
CPC classification number: G06K9/6218 , G06K9/00677 , G06K9/6219 , G06K9/6267 , G06K9/66
Abstract: A hierarchy of clusters is determined, where each leave of the hierarchy corresponds to one of the images in a group, and each cluster in the hierarchy identifies images in the group that are deemed similar to one another. The hierarchy identifies a similarity between each of the plurality of clusters.
Abstract translation: 确定集群的层次结构，其中层次结构的每个离开对应于组中的一个图像，并且层次中的每个集合标识组中认为彼此相似的图像。 该层次结构标识多个聚类中的每一个之间的相似性。
-
公开(公告)号：US20140172881A1
公开(公告)日：2014-06-19
申请号：US14185392
申请日：2014-02-20
Applicant: Google Inc.
Inventor： David Petrou , Andrew Rabinovich , Hartwig Adam
IPC: G06F17/30
CPC classification number: G06F17/30268 , G06F17/30256 , G06F17/30277 , G06F17/3053 , G06F17/30867 , G06K9/00288 , G06K9/00677
Abstract: A facial recognition search system identifies one or more likely names (or other personal identifiers) corresponding to the facial image(s) in a query as follows. After receiving the visual query with one or more facial images, the system identifies images that potentially match the respective facial image in accordance with visual similarity criteria. Then one or more persons associated with the potential images are identified. For each identified person, person-specific data comprising metrics of social connectivity to the requester are retrieved from a plurality of applications such as communications applications, social networking applications, calendar applications, and collaborative applications. An ordered list of persons is then generated by ranking the identified persons in accordance with at least metrics of visual similarity between the respective facial image and the potential image matches and with the social connection metrics. Finally, at least one person identifier from the list is sent to the requester.
Abstract translation: 面部识别搜索系统如下所述识别与查询中的面部图像相对应的一个或多个可能的名称（或其他个人标识符）。 在接收到具有一个或多个面部图像的视觉查询之后，系统根据视觉相似性标准识别潜在地匹配相应面部图像的图像。 然后识别与潜在图像相关联的一个或多个人。 对于每个已识别的人，从诸如通信应用，社交网络应用，日历应用和协作应用的多个应用中检索包括对请求者的社交连接性度量的个人特定数据。 然后通过根据相应的面部图像和潜在图像匹配之间的视觉相似性的度量以及与社会连接度量进行评估来对所识别的人进行排序来生成人的有序列表。 最后，从列表中至少有一个人标识符被发送到请求者。
-
-
-
-
-
-
-
-
-
Search Results
10
records
(took 0.036 seconds)
