-
公开(公告)号：US20170243082A1
公开(公告)日：2017-08-24
申请号：US15504870
申请日：2015-06-19
Applicant: Google Inc.
Inventor： Yang Song , Jiang Wang , Charles J. Rosenberg
CPC classification number: G06K9/6215 , G06F17/30256 , G06F17/3028 , G06K9/6212 , G06K9/627 , G06K9/66 , G06N3/0454 , G06N99/005
Abstract: Methods, systems, and apparatus, for determining fine-grained image similarity. In one aspect, a method includes training an image embedding function on image triplets by selecting image triplets of first, second and third images; generating, by the image embedding function, a first, second and third representations of the features of the first, second and third images; determining, based on the first representation of features and the second representation of features, a first similarity measure for the first image to the second image; determining, based on the first representation of features and the third representation of features, a second similarity measure for the first image to the third image; determining, based on the first and second similarity measures, a performance measure of the image embedding function for the image triplet; and adjusting the parameter weights of the image embedding function based on the performance measures for the image triplets.
-
公开(公告)号：US09305025B2
公开(公告)日：2016-04-05
申请号：US13827000
申请日：2013-03-14
Applicant: Google Inc.
Inventor： Jingbin Wang , Xiangrong Chen , Charles J. Rosenberg
IPC: G06F17/30
CPC classification number: G06F17/3053 , G06F17/30253 , G06F17/30268 , G06F17/30887
Abstract: Methods, systems, and apparatus, include computer programs encoded on a computer-readable storage medium, for determining keywords for an image that supports an overlay content item. A method includes identifying, using one or more processors, an image that is to support an overlay content item, the image being presented on a web site and including a portion that is designated as being enabled to receive and display the overlay content item; evaluating pixel data associated with the image including determining one or more labels that are associated with content included within the image; and determining one or more keywords for the image based at least in part on the one or more labels.
Abstract translation: 方法，系统和装置包括编码在计算机可读存储介质上的计算机程序，用于确定支持覆盖内容项的图像的关键字。 一种方法包括：使用一个或多个处理器识别支持覆盖内容项的图像，所述图像呈现在网站上，并且包括被指定为被启用以接收和显示所述重叠内容项的部分; 评估与所述图像相关联的像素数据，包括确定与所述图像内包含的内容相关联的一个或多个标签; 以及至少部分地基于所述一个或多个标签来确定所述图像的一个或多个关键字。
-
公开(公告)号：US09152652B2
公开(公告)日：2015-10-06
申请号：US13828254
申请日：2013-03-14
Applicant: Google Inc.
Inventor： Kunlong Gu , Charles J. Rosenberg , Mingchen Gao , Thomas J. Duerig
IPC: G06F17/30
CPC classification number: G06F17/30244
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for identifying images responsive to a search phrase are disclosed. In one aspect, a method includes identifying a set of responsive images for a search phrase that includes two or more terms. Interaction rankings are determined for images in the set of responsive images. Two or more sub-queries are created based on the search phrase. Sub-query model rankings are determined for images in the set of responsive images. A search phrase score is determined for the image relevance model. Based on the search phrase scores for the sub-queries, one of the sub-query models is selected as a model for the search phrase.
Abstract translation: 公开了方法，系统和装置，包括在计算机存储介质上编码的计算机程序，用于方法，系统和装置，包括编码在计算机存储介质上的计算机程序，用于响应于搜索短语识别图像。 一方面，一种方法包括识别包括两个或多个术语的搜索短语的一组响应图像。 确定响应图像集中的图像的相互作用排名。 基于搜索短语创建两个或多个子查询。 确定响应图像集中的图像的子查询模型排名。 确定图像相关性模型的搜索短语得分。 基于子查询的搜索短语分数，选择一个子查询模型作为搜索短语的模型。
-
公开(公告)号：US20150161170A1
公开(公告)日：2015-06-11
申请号：US14336692
申请日：2014-07-21
Applicant: Google Inc.
Inventor： Yangli Hector Yee , Samy Bengio , Charles J. Rosenberg , Erik Murphy-Chutorian
CPC classification number: G06F17/30253 , G06F17/30244 , G06F17/30864 , G06K9/4676 , G06K9/6256 , G06K9/66 , G06K2209/01
Abstract: An image classification system trains an image classification model to classify images relative to text appearing with the images. Training images are iteratively selected and classified by the image classification model according to feature vectors of the training images. An independent model is trained for unique n-grams of text. The image classification system obtains text appearing with an image and parses the text into candidate labels for the image. The image classification system determines whether an image classification model has been trained for the candidate labels. When an image classification model corresponding to a candidate label has been trained, the image classification subsystem classifies the image relative to the candidate label. The image is labeled based on candidate labels for which the image is classified as a positive image.
Abstract translation: 图像分类系统训练图像分类模型，以便相对于与图像一起出现的文本来分类图像。 根据训练图像的特征向量，通过图像分类模型迭代地选择和分类训练图像。 对独特的n克文本进行了独立的模型训练。 图像分类系统获得与图像一起出现的文本，并将文本解析为图像的候选标签。 图像分类系统确定是否针对候选标签训练了图像分类模型。 当对应于候选标签的图像分类模型已经被训练时，图像分类子系统将图像相对于候选标签进行分类。 基于将图像分类为正图像的候选标签来标记图像。
-
公开(公告)号：US08787683B1
公开(公告)日：2014-07-22
申请号：US13910493
申请日：2013-06-05
Applicant: Google Inc.
Inventor： Yangli Hector Yee , Samy Bengio , Charles J. Rosenberg , Erik Murphy-Chutorian
CPC classification number: G06F17/30253 , G06F17/30244 , G06F17/30864 , G06K9/4676 , G06K9/6256 , G06K9/66 , G06K2209/01
Abstract: An image classification system trains an image classification model to classify images relative to text appearing with the images. Training images are iteratively selected and classified by the image classification model according to feature vectors of the training images. An independent model is trained for unique n-grams of text. The image classification system obtains text appearing with an image and parses the text into candidate labels for the image. The image classification system determines whether an image classification model has been trained for the candidate labels. When an image classification model corresponding to a candidate label has been trained, the image classification subsystem classifies the image relative to the candidate label. The image is labeled based on candidate labels for which the image is classified as a positive image.
Abstract translation: 图像分类系统训练图像分类模型，以便相对于与图像一起出现的文本来分类图像。 根据训练图像的特征向量，通过图像分类模型迭代地选择和分类训练图像。 对独特的n克文本进行了独立的模型训练。 图像分类系统获得与图像一起出现的文本，并将文本解析为图像的候选标签。 图像分类系统确定是否针对候选标签训练了图像分类模型。 当对应于候选标签的图像分类模型已经被训练时，图像分类子系统将图像相对于候选标签进行分类。 基于将图像分类为正图像的候选标签来标记图像。
-
Patent Agency Ranking
公开(公告)号：US09792304B1
公开(公告)日：2017-10-17
申请号：US14946027
申请日：2015-11-19
Applicant: Google Inc.
Inventor： Ulrich Buddemeier , Gabriel Taubman , Hartwig Adam , Charles J. Rosenberg , Hartmut Neven , David Petrou , Fernando Brucher
IPC: G06F17/30
CPC classification number: G06F17/30277 , G06F17/30256 , G06F17/30268 , G06F17/3053 , G06F17/30554 , G06K9/6215 , G06K9/6282 , G06K9/723 , G06K2209/01
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing queries made up of images. In one aspect, a method includes indexing images by image descriptors. The method further includes associating descriptive n-grams with the images. In another aspect, a method includes receiving a query, identifying text describing the query, and performing a search according to the text identified for the query.
-
公开(公告)号：US09275310B2
公开(公告)日：2016-03-01
申请号：US14449262
申请日：2014-08-01
Applicant: Google Inc.
Inventor： Yang Song , Charles J. Rosenberg , Andrew Yan-Tak Ng , Bo Chen
CPC classification number: G06K9/68 , G06K9/46 , G06K9/623 , G06K9/6267
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for creating an image similarity model. In one aspect, a method includes obtaining feature vectors for images in a set of images, and determining first similarity measures for unlabeled images relative to a reference image. The first similarity measures are independent of first similarity feedback between the unlabeled images and the reference image. The unlabeled images are ranked based on the first similarity measures, and a weighted feature vector is generated based, in part, on the ranking. Second similarity measures are determined, independent of second similarity feedback, for labeled images and a second reference image. The labeled images are ranked based on the second similarity measures. The weighted feature vector is adjusted based, in part, on a comparison of the ranking to a second ranking of the labeled images that is based on the second similarity feedback.
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，用于创建图像相似性模型。 一方面，一种方法包括获得一组图像中的图像的特征向量，以及确定相对于参考图像的未标记图像的第一相似性度量。 第一相似性度量与未标记图像和参考图像之间的第一相似性反馈无关。 基于第一相似性度量对未标记的图像进行排序，并且部分地基于排名生成加权特征向量。 对于标记图像和第二参考图像，确定与第二相似性反馈无关的第二相似性度量。 基于第二相似性度量对标记图像进行排序。 部分地基于基于第二相似性反馈的标记图像的排名与第二排名的比较来调整加权特征向量。
-
公开(公告)号：US09251171B2
公开(公告)日：2016-02-02
申请号：US13690404
申请日：2012-11-30
Applicant: Google Inc.
Inventor： Zhongli Ding , Gabriel Wolosin , John R. Zhang , Charles J. Rosenberg , Yang Song
CPC classification number: G06F17/30256 , G06F17/30244
Abstract: Methods, systems and apparatus for identifying modified images based on seed images that are known to be modified images. In an aspect, a method includes accessing data identifying a set of first seed images; for each first seed image, determining a respective first set of similar images from images in an image corpus, each similar image having a visual similarity score that is a measure of visual similarity of the similar image to the first seed image based on the image content of the similar image and the first seed image that satisfies a first seed image similarity threshold; and for each similar image in each respective first set of similar images, attributing to the similar image signal data of each first seed image for which the similar image has a respective visual similarity score satisfying the first seed image similarity threshold.
Abstract translation: 用于基于已知是修改图像的种子图像识别修改图像的方法，系统和装置。 一方面，一种方法包括访问识别一组第一种子图像的数据; 对于每个第一种子图像，从图像语料库中的图像确定相应的第一组相似图像，每个相似图像具有视觉相似度得分，其是基于图像内容的类似图像与第一种子图像的视觉相似度的量度 以及满足第一种子图像相似性阈值的第一种子图像; 并且对于每个相应的第一组相似图像中的每个相似图像，归因于类似图像具有满足第一种子图像相似性阈值的相应视觉相似性得分的每个第一种子图像的相似图像信号数据。
-
公开(公告)号：US09176988B2
公开(公告)日：2015-11-03
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
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。
-
公开(公告)号：US20150161168A1
公开(公告)日：2015-06-11
申请号：US14059157
申请日：2013-10-21
Applicant: Google Inc.
Inventor： Michele Covell , Yushi Jing , Jingbin Wang , Charles J. Rosenberg , Sarah Moussa
IPC: G06F17/30
CPC classification number: G06F16/583 , G06F16/58 , G06F16/951
Abstract: Methods, systems, and apparatus, including computer program products, for ranking images are disclosed. An image search subsystem generates an adjustment factor representative of a quality measure of an image relative to a search query. The quality represents a relevance of the image to the query. The adjustment factor can be computed based on relevance data for the image to the query and image similarity data representing a relative similarity between the image and other images relevant to the query. The relevance data can be based on user actions in response to the image being included in search results for the query. The adjustment factor can be scaled based on whether the relevance data and the image similarity data both indicate that the image is relevant to the search query. A relevance score is computed based on the adjustment factor (e.g., a product of the adjustment factor and relevance score).
Abstract translation: 公开了用于对图像排序的方法，系统和装置，包括计算机程序产品。 图像搜索子系统生成表示相对于搜索查询的图像的质量度量的调整因子。 质量表示图像与查询的相关性。 调整因子可以基于图像与查询的相关性数据计算，图像相似度数据表示图像和与查询相关的其他图像之间的相对相似度。 相关数据可以基于响应于图像被包括在查询的搜索结果中的用户动作。 可以基于相关性数据和图像相似性数据是否都指示图像与搜索查询相关来来调整调整因子。 基于调整因子（例如，调整因子和相关性得分的乘积）来计算相关性得分。
-
-
-
-
-
-
-
-
-
Search Results
37
records
(took 0.037 seconds)
