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公开(公告)号：US09177046B2
公开(公告)日：2015-11-03
申请号：US14543312
申请日：2014-11-17
Applicant: Google Inc.
Inventor： Arcot J. Preetham , Thomas J. Duerig , Charles J. Rosenberg , Yangli Hector Yee , Samy Bengio
CPC classification number: G06F17/30675 , G06F17/3028 , G06K9/52 , G06K9/6202 , G06K9/6256 , G06K9/6262 , G06K9/6296 , G06K9/66
Abstract: Methods, systems and apparatus for refining image relevance models. In general, one aspect of the subject matter described in this specification can be implemented in methods that include re-training an image relevance model by generating a first re-trained model based on content feature values of first images of a first portion of training images in a set of training images, receiving, from the first re-trained model, image relevance scores for second images of a second portion of the set of training images, removing, from the set of training images, some of the second images identified as outlier images for which the image relevance score received from the first re-trained model is below a threshold score, and generating a second re-trained model based on content feature values of the first images of the first portion and the second images of the second portion that remain following removal of the outlier images.
Abstract translation: 图像相关模型的方法，系统和装置。 通常，本说明书中描述的主题的一个方面可以以包括通过基于训练图像的第一部分的第一图像的内容特征值生成第一重新训练的模型来重新训练图像相关性模型的方法来实现 在一组训练图像中，从所述第一重新训练的模型中接收所述训练图像集合的第二部分的第二图像的图像相关性分数，从所述训练图像集合中去除被识别为 从第一重新训练的模型接收的图像相关性得分低于阈值分数的异常值图像，并且基于第一部分的第一图像和第二图像的第二图像的内容特征值生成第二重新训练的模型 删除离群图像后仍保留的部分。
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公开(公告)号：US08923655B1
公开(公告)日：2014-12-30
申请号：US13650489
申请日：2012-10-12
Applicant: Google Inc.
Inventor： Jason Weston , Samy Bengio
IPC: G06K9/60
CPC classification number: G06F17/30274
Abstract: A server device determines a plurality of images for a query. One or more images, of the plurality of images, are associated with one or more senses of the query. The server device maps the plurality of images into a space by representing the plurality of images with corresponding points in the space; determines one or more hyperplanes in the space based on the corresponding points in the space; calculates one or more scores for the plurality of images based on the corresponding points and the one or more hyperplanes; and ranks the one or more images based on the one or more scores.
Abstract translation: 服务器设备确定用于查询的多个图像。 多个图像中的一个或多个图像与查询的一个或多个感觉相关联。 服务器设备通过用空间中的相应点表示多个图像来将多个图像映射到空间中; 基于空间中的对应点确定空间中的一个或多个超平面; 基于对应点和一个或多个超平面来计算多个图像的一个或多个分数; 并根据一个或多个分数对一个或多个图像进行排序。
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公开(公告)号：US09727584B2
公开(公告)日：2017-08-08
申请号：US14498323
申请日：2014-09-26
Applicant: Google Inc.
Inventor： Neil G. Alldrin , Charles J. Rosenberg , Bin Shen , Samy Bengio , Zhen Hao Zhou
CPC classification number: G06F17/30268 , G06K9/00664
Abstract: Methods, systems and apparatus for refining image annotations. In one aspect, a method includes receiving, for each image in a set of images, a corresponding set of labels determined to be indicative of subject matter of the image. For each label, one or more confidence values are determined. Each confidence value is a measure of confidence that the label accurately describes the subject matter of a threshold number of respective images to which it corresponds. Labels for which each of the one or more confidence values meets a respective confidence threshold are identified as high confidence labels. For each image in the set of images, labels in its corresponding set of labels that are high confidence labels are identified. Images having a corresponding set of labels that include at least a respective threshold number of high confidence labels are identified as high confidence images.
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公开(公告)号：US09652695B2
公开(公告)日：2017-05-16
申请号：US14135816
申请日：2013-12-20
Applicant: Google Inc.
Inventor： Samy Bengio , Jeffrey Adgate Dean , Quoc Le , Jonathon Shlens , Yoram Singer
CPC classification number: G06K9/723 , G06K9/00677 , G06K9/6212 , G06K9/72 , G06K2009/6213 , G06N7/005 , G06N99/005
Abstract: Systems and techniques for labeling objects within an image. The objects may be labeled by selecting an option from a plurality of options such that each option is a potential label for the object. An option may have an option score associated with. Additionally, a relation score may be calculated for a first option and a second option corresponding to a second object in an image. The relation score may be based on a frequency, probability, or observance corresponding to the co-occurrence of text associated with the first option and the second option in a text corpus such as the World Wide Web. An option may be selected as a label for an object based on a global score calculated based at least on an option score and relation score associated with the option.
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公开(公告)号：US09372920B2
公开(公告)日：2016-06-21
申请号：US14596081
申请日：2015-01-13
Applicant: Google Inc.
Inventor： Samy Bengio , David Petrou
CPC classification number: G06F17/30696 , G06F17/30268 , G06F17/30277 , G06F17/30663
Abstract: A method, system, and computer readable storage medium is provided for identifying textual terms in response to a visual query is provided. A server system receives a visual query from a client system. The visual query is responded to as follows. A set of image feature values for the visual query is generated. The set of image feature values is mapped to a plurality of textual terms, including a weight for each of the textual terms in the plurality of textual terms. The textual terms are ranked in accordance with the weights of the textual terms. Then, in accordance with the ranking the textual terms, one or more of the ranked textual terms are sent to the client system.
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Patent Agency Ranking
公开(公告)号：US20150178383A1
公开(公告)日：2015-06-25
申请号：US14576907
申请日：2014-12-19
Applicant: Google Inc.
Inventor： Gregory Sean Corrado , Tomas Mikolov , Samy Bengio , Yoram Singer , Jonathon Shlens , Andrea L. Frome , Jeffrey Adgate Dean , Mohammad Norouzi
IPC: G06F17/30
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for classifying data objects. One of the methods includes obtaining data that associates each term in a vocabulary of terms with a respective high-dimensional representation of the term; obtaining classification data for a data object, wherein the classification data includes a respective score for each of a plurality of categories, and wherein each of the categories is associated with a respective category label; computing an aggregate high-dimensional representation for the data object from high-dimensional representations for the category labels associated with the categories and the respective scores; identifying a first term in the vocabulary of terms having a high-dimensional representation that is closest to the aggregate high-dimensional representation; and selecting the first term as a category label for the data object.
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，用于对数据对象进行分类。 其中一种方法包括获得将术语词汇中的每个术语与该术语的相应高维表示相关联的数据; 获取数据对象的分类数据，其中分类数据包括多个类别中的每一个的相应分数，并且其中每个类别与相应的分类标签相关联; 从与类别和相应分数相关联的类别标签的高维表示中计算数据对象的聚合高维表示; 识别具有最接近聚合高维表示的高维表示的术语词汇表中的第一项; 并选择第一项作为数据对象的类别标签。
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公开(公告)号：US20150169641A1
公开(公告)日：2015-06-18
申请号：US14498323
申请日：2014-09-26
Applicant: Google Inc.
Inventor： Neil G. Alldrin , Charles J. Rosenberg , Bin Shen , Samy Bengio , Zhen Hao Zhou
IPC: G06F17/30
CPC classification number: G06F17/30268 , G06K9/00664
Abstract: Methods, systems and apparatus for refining image annotations. In one aspect, a method includes receiving, for each image in a set of images, a corresponding set of labels determined to be indicative of subject matter of the image. For each label, one or more confidence values are determined. Each confidence value is a measure of confidence that the label accurately describes the subject matter of a threshold number of respective images to which it corresponds. Labels for which each of the one or more confidence values meets a respective confidence threshold are identified as high confidence labels. For each image in the set of images, labels in its corresponding set of labels that are high confidence labels are identified. Images having a corresponding set of labels that include at least a respective threshold number of high confidence labels are identified as high confidence images.
Abstract translation: 改进图像注释的方法，系统和设备。 在一个方面，一种方法包括：对于图像集合中的每个图像，接收确定为指示图像主题的相应标签集合。 对于每个标签，确定一个或多个置信度值。 每个置信度值是对标签准确地描述其对应的各个图像的阈值数目的主题的置信度的度量。 将一个或多个置信度值中的每一个满足相应置信度阈值的标签识别为高置信度标签。 对于图像集合中的每个图像，识别其相应的标签组中的高置信度标签的标签。 具有包括至少相应的阈值数量的高置信度标签的相应标签组的图像被识别为高置信度图像。
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公开(公告)号：US20150161172A1
公开(公告)日：2015-06-11
申请号：US13966737
申请日：2013-08-14
Applicant: Google Inc
Inventor： Samy Bengio , Erik Murphy-Chutorian , Yangli Hector Yee , Charles J. Rosenberg
IPC: G06F17/30
CPC classification number: G06F17/30256 , G06F17/30271 , G06K9/6223 , G06K9/6262
Abstract: Methods, systems, and apparatus, including computer program products, for identifying images relevant to a query are disclosed. An image search subsystem selects images to reference in image search results that are responsive to a query based on an image relevance model that is trained for the query. An independent image relevance model is trained for each unique query that is identified by the image search subsystem. The image relevance models can be applied to images to order image search results obtained for the query. Each relevance model is trained based on content feature values of images that are identified as being relevant to the query (e.g., frequently selected from the image search results) and images that are identified as being relevant to another unique query. The trained model is applied to the content feature values of all known images to generate an image relevance score that can be used to order search results for the query.
Abstract translation: 公开了用于识别与查询相关的图像的方法，系统和装置，包括计算机程序产品。 图像搜索子系统基于针对查询进行训练的图像相关性模型，在响应于查询的图像搜索结果中选择图像进行参考。 对由图像搜索子系统识别的每个唯一查询训练独立的图像相关性模型。 图像相关性模型可以应用于图像以订购为查询获得的图像搜索结果。 基于被识别为与查询相关（例如，从图像搜索结果中频繁选择）的图像的内容特征值以及被识别为与另一唯一查询相关的图像来训练每个相关性模型。 经过训练的模型被应用于所有已知图像的内容特征值，以生成可用于对查询进行搜索结果的图像相关性分数。
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公开(公告)号：US09218573B1
公开(公告)日：2015-12-22
申请号：US13826327
申请日：2013-03-14
Applicant: Google Inc.
Inventor： Gregory S. Corrado , Kai Chen , Jeffrey A. Dean , Samy Bengio , Rajat Monga , Matthieu Devin
CPC classification number: G06N99/005 , G06K9/6256 , G06K9/6269 , G06N3/063 , G06N3/08 , G06N5/025 , G06N7/005 , G06N7/08
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a model using parameter server shards. One of the methods includes receiving, at a parameter server shard configured to maintain values of a disjoint partition of the parameters of the model, a succession of respective requests for parameter values from each of a plurality of replicas of the model; in response to each request, downloading a current value of each requested parameter to the replica from which the request was received; receiving a succession of uploads, each upload including respective delta values for each of the parameters in the partition maintained by the shard; and updating values of the parameters in the partition maintained by the parameter server shard repeatedly based on the uploads of delta values to generate current parameter values.
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，用于使用参数服务器分片训练模型。 其中一种方法包括在被配置为维持模型的参数的不相交分区的值的参数服务器分片上接收来自模型的多个副本中的每一个的参数值的相继请求; 响应于每个请求，将每个请求的参数的当前值下载到从其接收请求的副本; 接收连续的上传，每次上传包括由分片保存的分区中的每个参数的各自的增量值; 并且根据增量值的上载重复地更新由参数服务器分片保存的分区中的参数的值，以生成当前参数值。
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公开(公告)号：US20150193528A1
公开(公告)日：2015-07-09
申请号：US14596081
申请日：2015-01-13
Applicant: Google Inc.
Inventor： Samy Bengio , David Petrou
IPC: G06F17/30
CPC classification number: G06F17/30696 , G06F17/30268 , G06F17/30277 , G06F17/30663
Abstract: A method, system, and computer readable storage medium is provided for identifying textual terms in response to a visual query is provided. A server system receives a visual query from a client system. The visual query is responded to as follows. A set of image feature values for the visual query is generated. The set of image feature values is mapped to a plurality of textual terms, including a weight for each of the textual terms in the plurality of textual terms. The textual terms are ranked in accordance with the weights of the textual terms. Then, in accordance with the ranking the textual terms, one or more of the ranked textual terms are sent to the client system.
Abstract translation: 提供了一种方法，系统和计算机可读存储介质，用于识别响应于视觉查询提供的文本术语。 服务器系统从客户端系统接收可视化查询。 视觉查询响应如下。 生成视觉查询的一组图像特征值。 图像特征值的集合被映射到多个文本术语，包括多个文本术语中的每个文本术语的权重。 文本术语根据文本术语的权重进行排序。 然后，根据文本术语的排名，将一个或多个排名的文本术语发送到客户端系统。
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