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    Customizing image search for user attributes
    11.
    发明授权
    Customizing image search for user attributes 有权
    自定义图像搜索用户属性

    公开(公告)号：US08782029B1

    公开(公告)日：2014-07-15

    申请号：US13959233

    申请日：2013-08-05

    Applicant: Google Inc.

    Inventor： Yangli Hector Yee ,  Charles J. Rosenberg

    IPC: G06F17/30 ,  G06F7/00

    CPC classification number: G06F17/30864 ,  G06F17/30244 ,  G06F17/30268 ,  G06F17/3053

    Abstract: Systems, method, and apparatus including computer program products for providing image search results. In some implementations, a method is provided. The method includes receiving from a user a query for images including static images, moving images, and images within multimedia content, identifying at least one of a language attribute and a locale attribute of the user, generating multiple search results, each result corresponding to an image content item that satisfies the query, ordering the search results based at least on click data for image content items that satisfy the query, the click data gathered from users having at least one of the language attribute and the locale attribute, and presenting the ordered search results to the user, including presenting representations of the corresponding image content items.

    Abstract translation: 包括用于提供图像搜索结果的计算机程序产品的系统，方法和装置。 在一些实现中，提供了一种方法。 该方法包括从用户接收包括静态图像，运动图像和多媒体内容中的图像的图像的查询，识别用户的语言属性和区域设置属性中的至少一个，生成多个搜索结果，每个结果对应于 满足查询的图像内容项目，至少基于满足查询的图像内容项目的点击数据，从具有语言属性和语言环境属性中的至少一个的用户收集的点击数据来排序搜索结果，并呈现订购的图像内容项目 搜索结果给用户，包括呈现对应的图像内容项目的表示。

    Image selection based on image quality
    12.
    发明授权
    Image selection based on image quality 有权
    基于图像质量的图像选择

    公开(公告)号：US08738553B1

    公开(公告)日：2014-05-27

    申请号：US13758108

    申请日：2013-02-04

    Applicant: Google Inc.

    Inventor： Thomas Leung ,  Charles J. Rosenberg

    IPC: G06E1/00 ,  G06E3/00 ,  G06F15/18 ,  G06G7/00 ,  G06F17/30 ,  G06K9/62 ,  G06F3/0484

    CPC classification number: G06F17/30247 ,  G06K9/6228 ,  G06K9/6263

    Abstract: An image quality subsystem computes quality scores for images that represent a measure of visual quality of the images. Initial quality scores and query specific quality scores can be computed for the images based on image feature values for the images and a transformation factor that represents a measure of importance of image quality for computing relevance scores for images. The initial quality scores are query independent quality scores that are computed for the images and can be used as a factor for computing relevance scores for the image relative to any query. Query specific quality scores are computed for images that are identified as relevant for a particular query based on the initial quality scores and a query specific transformation factor for the particular query. Adjusted relevance scores for the images can be computed based on the initial quality scores or the query specific quality scores.

    Abstract translation: 图像质量子系统计算表示图像的视觉质量的度量的图像的质量分数。 可以基于图像的图像特征值对图像计算初始质量分数和查询特定质量分数，以及表示用于计算图像的相关性分数的图像质量的重要性的度量的变换因子。 初始质量得分是针对图像计算的查询独立质量得分，可用作计算图像相对于任何查询的相关性分数的因子。 基于特定查询的初始质量得分和查询特定变换因子计算针对特定查询相关的图像的查询特定质量得分。 可以根据初始质量得分或查询特定质量得分来计算图像的相关性得分。

    CONTENT SELECTION BASED ON IMAGE CONTENT
    13.
    发明申请
    CONTENT SELECTION BASED ON IMAGE CONTENT 有权
    基于图像内容的内容选择

    公开(公告)号：US20160179817A1

    公开(公告)日：2016-06-23

    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.

    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。

    Query image model
    14.
    发明授权
    Query image model 有权
    查询图像模型

    公开(公告)号：US09218366B1

    公开(公告)日：2015-12-22

    申请号：US14145100

    申请日：2013-12-31

    Applicant: Google Inc.

    Inventor： Congcong Li ,  Kunlong Gu ,  Charles J. Rosenberg

    IPC: G06F17/30

    CPC classification number: G06F17/30247 ,  G06F17/30256

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a query image model. In one aspect, a method includes receiving a set of images determined to be responsive to a query and ranked according to a first order; determining a positive image signature from a first subset of images selected from images ranked highest in the first order, determining a negative image signature from a second subset of images selected from images ranked lowest in the first order, determining a query image signature for the query based on a difference of the positive image signature and the negative image signature; and applying the query image signature to each image in the set of images to rank the images according to a second order that is different from the first order.

    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，用于生成查询图像模型。 一方面，一种方法包括接收确定为响应于查询并根据第一顺序进行排序的图像集合; 从根据所述第一顺序排列最高的图像的第一图像子集确定正图像签名，从选自所述第一顺序最低的图像的第二图像子集确定负图像签名，确定所述查询的查询图像签名 基于正图像签名和负图像签名的差异; 以及将所述查询图像签名应用于所述图像集合中的每个图像，以根据与所述第一顺序不同的第二顺序来对所述图像进行排序。

    Image classification
    15.
    发明授权
    Image classification 有权
    图像分类

    公开(公告)号：US09183226B2

    公开(公告)日：2015-11-10

    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

    IPC: G06K9/62 ,  G06K9/46 ,  G06K9/66 ,  G06K9/54 ,  G06K9/60 ,  G06F17/30

    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。

    Refining image relevance models
    16.
    发明授权
    Refining image relevance models 有权
    精炼图像相关模型

    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。

    ONLINE IMAGE ANALYSIS
    17.
    发明申请
    ONLINE IMAGE ANALYSIS 审中-公开
    在线图像分析

    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。

    NEAR DUPLICATE IMAGES
    19.
    发明申请
    NEAR DUPLICATE IMAGES 有权
    近似重复图像

    公开(公告)号：US20140105505A1

    公开(公告)日：2014-04-17

    申请号：US13832122

    申请日：2013-03-15

    Applicant: GOOGLE INC.

    Inventor： Sergey Ioffe ,  Mohamed Aly ,  Charles J. Rosenberg

    IPC: G06F17/30

    CPC classification number: G06F17/30247 ,  G06F17/3025 ,  G06K9/4676 ,  G06K9/6202

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining image search results. One of the methods includes generating a plurality of feature vectors for each image in a collection of images, wherein each feature vector is associated with an image tile of an image, wherein each feature vector corresponds to one of a plurality of predetermined visual words. All images in the collection of images that share at least a threshold number of matching visual words associated with matching image tiles are classified as near-duplicate 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，用于确定图像搜索结果。 一种方法包括为图像集合中的每个图像生成多个特征向量，其中每个特征向量与图像的图像块相关联，其中每个特征向量对应于多个预定视觉词中的一个。 共享与匹配的图像块相关联的至少阈值数量的匹配视觉词的图像集合中的所有图像被分类为近似重复的图像。

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