-
公开(公告)号：US20160070990A1
公开(公告)日：2016-03-10
申请号：US14940660
申请日：2015-11-13
Applicant: Google Inc.
Inventor： Yong Zhang , Charles J. Rosenberg , Jingbin Wang , Sean O Malley
CPC classification number: G06K9/6267 , G06F17/30253 , G06F17/3053 , G06K9/52 , G06K9/6201 , G06K9/6202 , G06K9/6218 , G06K9/68 , G06K9/74
Abstract: Methods, systems and apparatus for choosing image labels. In one aspect, a method includes receiving data specifying a first image, receiving text labels for the first image, receiving search results in response to a web search performed using at least some of the text labels as queries, ranking the text labels, at least in part, based on a number of resources referenced by the received search results, wherein at least some of the resources each include an image matching the first image, and selecting an image label for the image from the ranked text labels, the image label being selected based on the ranking.
-
公开(公告)号：US09201903B2
公开(公告)日：2015-12-01
申请号：US14301154
申请日：2014-06-10
Applicant: Google Inc.
Inventor： Ulrich Buddemeier , Gabriel Taubman , Hartwig Adam , Charles J. Rosenberg , Hartmut Neven , David Petrou , Fernando Brucher
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.
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，用于处理由图像组成的查询。 一方面，一种方法包括通过图像描述符索引图像。 该方法还包括将描述性n-gram与图像相关联。 在另一方面，一种方法包括接收查询，识别描述查询的文本，以及根据为查询标识的文本执行搜索。
-
公开(公告)号：US09183460B2
公开(公告)日：2015-11-10
申请号：US13690381
申请日：2012-11-30
Applicant: Google Inc.
Inventor： John R. Zhang , Charles J. Rosenberg , Yang Song
IPC: G06K9/62
CPC classification number: G06K9/6202 , G06K9/6212 , G06K2009/6213
Abstract: Methods, systems and apparatus for identifying modified images based on visual dissimilarity to a first image. In an aspect, a method includes determining, for each of a first image and a second image, a respective set of local image feature descriptions; determining one or more unmatched regions of the images that include unmatched image features and that correspond to one or more same respective regions in both the first image and the second image; determining, for each of the one or more unmatched regions of the images, a modification measure based on the image data corresponding to the unmatched region in the first image and the image data corresponding to the unmatched region in the second image; and determining that the second image is a modification of the first image when one of the modification measures meets a modification measure threshold.
Abstract translation: 用于基于与第一图像的视觉差异来识别修改图像的方法，系统和装置。 在一方面，一种方法包括为第一图像和第二图像中的每一个确定相应的局部图像特征描述集合; 确定包括不匹配的图像特征并且对应于第一图像和第二图像中的一个或多个相同的相应区域的图像的一个或多个不匹配的区域; 基于与第一图像中的不匹配区域相对应的图像数据和对应于第二图像中的不匹配区域的图像数据，为图像的一个或多个不匹配区域中的每一个确定修改度量; 以及当所述修改措施之一满足修改度量阈值时，确定所述第二图像是所述第一图像的修改。
-
公开(公告)号：US20150170004A1
公开(公告)日：2015-06-18
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：US20150169930A1
公开(公告)日：2015-06-18
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
IPC: G06K9/00
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。
-
Patent Agency Ranking
公开(公告)号：US20150169631A1
公开(公告)日：2015-06-18
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：US20150161482A1
公开(公告)日：2015-06-11
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。
-
公开(公告)号：US08903182B1
公开(公告)日：2014-12-02
申请号：US13666083
申请日：2012-11-01
Applicant: Google Inc.
Inventor： Thomas J. Duerig , Charles J. Rosenberg , Kunlong Gu , Samy Bengio
CPC classification number: G06F17/30247
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for classifying images. In one aspect, a method includes receiving training samples for a particular data dimension. Each training sample specifies a training value for the data dimension and a measure of relevance between the training sample and a phrase. A value range is determined for the data dimension. The value range is segmented into two or more segments. A predictive model is trained for each segment. The predictive model for each segment is trained to predict an output based on an input value that is within the segment. A classification sample specifying an input value is received. A classification output is computed based on the input value, the predictive model for the segment in which the input value is included, and the predictive model for an adjacent segment.
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，用于分类图像。 一方面，一种方法包括接收特定数据维度的训练样本。 每个训练样本指定数据维度的训练值和训练样本与短语之间的相关性度量。 确定数据维度的值范围。 值范围分为两个或多个段。 对每个细分受训的预测模型。 对每个段的预测模型进行训练，以基于段内的输入值来预测输出。 接收指定输入值的分类样本。 基于输入值，包含输入值的段的预测模型和相邻段的预测模型来计算分类输出。
-
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.
-
公开(公告)号：US09436737B2
公开(公告)日：2016-09-06
申请号：US15053786
申请日：2016-02-25
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.
-
-
-
-
-
-
-
-
-
Search Results
37
records
(took 0.027 seconds)
