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：US20150169708A1

公开(公告)日：2015-06-18

申请号：US14249523

申请日：2014-04-10

Applicant: Google Inc.

Inventor： Yang Song ,  Thomas J. Duerig

IPC: G06F17/30

CPC classification number: G06F16/54 ,  G06F16/50 ,  G06F16/583 ,  G06F16/951

Abstract: A computer implemented technique for presenting selected image search results is presented. The technique includes obtaining a first query at a first time and obtaining a first set of image search results responsive to the first query. The technique also includes providing the first set of image search results in response to the first query and obtaining input data reflecting a selection of at least one of the first set of image search results. The technique further includes obtaining a second query at a second time subsequent to the first time and obtaining a second set of image search results responsive to the second query. The technique further includes providing the second set of image search results together with the selected at least one of the first set of image search results.

Abstract translation: 提出了一种用于呈现所选图像搜索结果的计算机实现技术。 该技术包括在第一时间获得第一查询并且响应于第一查询获得第一组图像搜索结果。 该技术还包括响应于第一查询提供第一组图像搜索结果并获得反映第一组图像搜索结果中的至少一个的选择的输入数据。 该技术还包括在第一次之后的第二时间获得第二查询，并且响应于第二查询获得第二组图像搜索结果。 该技术还包括提供第二组图像搜索结果以及所选择的第一组图像搜索结果中的至少一个。

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

IPC: G06K9/62 ,  G06F17/30

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

IPC: G06K9/62 ,  G06K9/46 ,  G06K9/54

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：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

IPC: G06K9/62 ,  G06F17/30 ,  G06K9/66 ,  G06K9/52

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：US20150169754A1

公开(公告)日：2015-06-18

申请号：US13780848

申请日：2013-02-28

Applicant: Google Inc.

Inventor： Kunlong Gu ,  Sean Arietta ,  Charles J. Rosenberg ,  Thomas J. Duerig ,  Erik Murphy-Chutorian

IPC: G06F17/30

CPC classification number: G06F17/30256

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for analyzing image search result relevance. In one aspect, a method includes receiving result data specifying a search query and responsive image search results that reference images that are responsive to the search query. A determination is made that the search query matches an indexed query. An image relevance model is identified for the indexed query. The image relevance model can output a relevance score adjustment factor for an image search result based on image feature values of the image that is referenced by the search result. A relevance score adjustment factor is determined for each image search result using the identified image relevance model. A relevance score for each image search result is adjusted using the image's image relevance score adjustment factor. The images are ranked based on the adjusted relevance scores.

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，用于分析图像搜索结果相关性。 一方面，一种方法包括接收指定搜索查询的结果数据和引用响应于搜索查询的图像的响应图像搜索结果。 确定搜索查询与索引查询匹配。 为索引查询识别图像相关性模型。 图像相关性模型可以基于由搜索结果引用的图像的图像特征值输出图像搜索结果的相关性分数调整因子。 使用所识别的图像相关性模型，针对每个图像搜索结果确定相关性得分调整因子。 使用图像的图像相关性得分调整因子来调整每个图像搜索结果的相关性分数。 图像根据调整的相关性分数进行排名。

公开(公告)号：US08938449B1

公开(公告)日：2015-01-20

申请号：US13886726

申请日：2013-05-03

Applicant: Google Inc.

Inventor： Thomas J. Duerig

IPC: G06F17/30

CPC classification number: G06F17/3053 ,  G06F17/30244 ,  G06F17/30265 ,  G06K9/00684

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for adjusting confidence scores of image labels for images. In one aspect, a method includes accessing images stored in an image data store, the images being associated with respective sets of labels, the labels describing content depicted in the image and having a respective confidence score that is a measure of confidence that the label accurately describes the content depicted in the image; selecting a first image from the images and determining for each of the other images and independent of the labels, a proximity score that is a measure of a relatedness of the other image to the first image; and adjusting the set of labels associated with the first image based on the respective proximity scores of the other images and the confidence scores of the labels of the other images.

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，用于调整用于图像的图像标签的置信度分数。 在一个方面，一种方法包括访问存储在图像数据存储器中的图像，所述图像与各组标签相关联，所述标签描述图像中描绘的内容，并且具有相应的置信度分数，其是所述标签的准确度的置信度 描述图像中描绘的内容; 从所述图像中选择第一图像并且确定每个所述其它图像并且独立于所述标签，所述邻近分数是所述另一图像与所述第一图像的相关性的量度; 以及基于其他图像的相应接近度分数和其他图像的标签的置信度得分来调整与第一图像相关联的标签组。