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公开(公告)号：US09454600B2
公开(公告)日：2016-09-27
申请号：US13363979
申请日：2012-02-01
CPC分类号： G06F17/30675 , G06F17/3028 , G06K9/52 , G06K9/6202 , G06K9/6256 , G06K9/6262 , G06K9/6296 , G06K9/66
摘要： Methods, systems and apparatus for refining image relevance models. In general, one aspect includes receiving a trained image relevance model that generates relevance measures of content feature values of images to a query, identifying a first threshold number of common content feature values for the set of training images, the common content feature values being identified as a set of content feature values that are each shared by at least a portion of the training images, identifying a subset of the set of training images having a quantity of the common content feature values greater than a second threshold number of content features values, and generating a re-trained image relevance model based on content feature values of the set of training images, wherein content feature values of the subset of training images are weighted higher than content feature values of the training images not in the subset.
摘要翻译： 图像相关模型的方法，系统和装置。 通常，一个方面包括接收经过训练的图像相关性模型，该模型生成图像的内容特征值的相关性度量到查询，识别该组训练图像的公共内容特征值的第一阈值数量，所识别的共同内容特征值 作为由训练图像的至少一部分共享的一组内容特征值，识别具有大于第二阈值数量的内容特征值的公共内容特征值的量的训练图像集合的子集， 以及基于训练图像集合的内容特征值生成重新训练的图像相关性模型，其中训练图像的子集的内容特征值被加权高于不在该子集中的训练图像的内容特征值。
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公开(公告)号：US20150169999A1
公开(公告)日：2015-06-18
申请号：US13363979
申请日：2012-02-01
CPC分类号： G06F17/30675 , G06F17/3028 , G06K9/52 , G06K9/6202 , G06K9/6256 , G06K9/6262 , G06K9/6296 , G06K9/66
摘要： Methods, systems and apparatus for refining image relevance models. In general, one aspect includes receiving a trained image relevance model that generates relevance measures of content feature values of images to a query, identifying a first threshold number of common content feature values for the set of training images, the common content feature values being identified as a set of content feature values that are each shared by at least a portion of the training images, identifying a subset of the set of training images having a quantity of the common content feature values greater than a second threshold number of content features values, and generating a re-trained image relevance model based on content feature values of the set of training images, wherein content feature values of the subset of training images are weighted higher than content feature values of the training images not in the subset.
摘要翻译： 图像相关模型的方法，系统和装置。 通常，一个方面包括接收经过训练的图像相关性模型，该模型生成图像的内容特征值的相关性度量到查询，识别该组训练图像的公共内容特征值的第一阈值数量，所识别的共同内容特征值 作为由训练图像的至少一部分共享的一组内容特征值，识别具有大于第二阈值数量的内容特征值的公共内容特征值的量的训练图像集合的子集， 以及基于训练图像集合的内容特征值生成重新训练的图像相关性模型，其中训练图像的子集的内容特征值被加权高于不在该子集中的训练图像的内容特征值。
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公开(公告)号：US09152700B2
公开(公告)日：2015-10-06
申请号：US13350143
申请日：2012-01-13
申请人： Thomas J. Duerig , Charles J. Rosenberg , Kunlong Gu , Samy Bengio , Yun Zhou
发明人： Thomas J. Duerig , Charles J. Rosenberg , Kunlong Gu , Samy Bengio , Yun Zhou
CPC分类号： G06F17/30675 , G06F17/3028 , G06K9/52 , G06K9/6202 , G06K9/6256 , G06K9/6262 , G06K9/6296 , G06K9/66
摘要： A method includes receiving a search query comprising one or more query terms, receiving image relevance models that each generate relevance measures of content feature values of images to a query, each image relevance model being a predictive model that has been trained based on content feature values of a set of training images, and each of the queries being a unique set of one or more query terms received by a search system as a query input, identifying an image relevance model for a different query that has been identified as similar to the received search query, and calculating a fractional adjustment multiplier for search results responsive to the received search query, the fractional adjustment multiplier being based on a relevance measure generated by the identified image relevance model for the different query and based on a degree of similarity between the different query and the received search query.
摘要翻译： 一种方法包括接收包括一个或多个查询项的搜索查询，接收图像相关性模型，每个图像相关性模型生成图像的内容特征值的相关性度量到查询，每个图像相关性模型是已经基于内容特征值训练的预测模型 的一组训练图像，并且每个查询是由搜索系统作为查询输入接收的一个或多个查询项的唯一集合，识别已经被识别为类似于所接收到的不同查询的图像相关性模型 搜索查询，以及响应于所接收的搜索查询来计算用于搜索结果的分数调整乘数，所述分数调整乘数基于由针对所述不同查询的所识别的图像相关性模型生成的相关性度量，并且基于所述不同查询的相似度 查询和收到的搜索查询。
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公开(公告)号：US20150169738A1
公开(公告)日：2015-06-18
申请号：US13350143
申请日：2012-01-13
申请人： Thomas J. Duerig , Charles J. Rosenberg , Kunlong Gu , Samy Bengio , Yun Zhou
发明人： Thomas J. Duerig , Charles J. Rosenberg , Kunlong Gu , Samy Bengio , Yun Zhou
IPC分类号： G06F17/30
CPC分类号： G06F17/30675 , G06F17/3028 , G06K9/52 , G06K9/6202 , G06K9/6256 , G06K9/6262 , G06K9/6296 , G06K9/66
摘要： A method includes receiving a search query comprising one or more query terms, receiving image relevance models that each generate relevance measures of content feature values of images to a query, each image relevance model being a predictive model that has been trained based on content feature values of a set of training images, and each of the queries being a unique set of one or more query terms received by a search system as a query input, identifying an image relevance model for a different query that has been identified as similar to the received search query, and calculating a fractional adjustment multiplier for search results responsive to the received search query, the fractional adjustment multiplier being based on a relevance measure generated by the identified image relevance model for the different query and based on a degree of similarity between the different query and the received search query.
摘要翻译： 一种方法包括接收包括一个或多个查询项的搜索查询，接收图像相关性模型，每个图像相关性模型生成图像的内容特征值的相关性度量到查询，每个图像相关性模型是已经基于内容特征值训练的预测模型 的一组训练图像，并且每个查询是由搜索系统作为查询输入接收的一个或多个查询项的唯一集合，识别已经被识别为类似于所接收到的不同查询的图像相关性模型 搜索查询，以及响应于所接收的搜索查询来计算用于搜索结果的分数调整乘数，所述分数调整乘数基于由针对所述不同查询的所识别的图像相关性模型生成的相关性度量，并且基于所述不同查询的相似度 查询和收到的搜索查询。
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公开(公告)号：US08891858B1
公开(公告)日：2014-11-18
申请号：US13545222
申请日：2012-07-10
CPC分类号： G06F17/30675 , G06F17/3028 , G06K9/52 , G06K9/6202 , G06K9/6256 , G06K9/6262 , G06K9/6296 , G06K9/66
摘要： 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.
摘要翻译： 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：US08873867B1
公开(公告)日：2014-10-28
申请号：US13545373
申请日：2012-07-10
申请人： Neil G. Alldrin , Thomas J. Duerig , Zhen Hao Zhou , Maks Ovsjanikovs , Charles J. Rosenberg , Samy Bengio , Maya Gupta
发明人： Neil G. Alldrin , Thomas J. Duerig , Zhen Hao Zhou , Maks Ovsjanikovs , Charles J. Rosenberg , Samy Bengio , Maya Gupta
CPC分类号： G06K9/726 , G06F17/30247 , G06K2209/27
摘要： Methods, systems and apparatus for assigning labels to images. In general, in one aspect, a method includes determining, for an image, a first set of labels, each label being determined to be indicative of subject matter of the image based on content feature values of the image, for each label in the first set of labels, determining a second set of labels, each label in the second set of labels determined to be semantically related to the label in the first set of labels, assigning a score to each label in the second sets of labels, and based on the scores assigned to each label in the second sets of labels, assigning one or more of the labels in the second sets of labels to the image.
摘要翻译： 用于为图像分配标签的方法，系统和装置。 通常，在一个方面，一种方法包括针对图像确定第一组标签，每个标签被确定为基于图像的内容特征值来指示图像的主题，对于第一组标签中的每个标签 确定第二组标签，第二组标签中的每个标签被确定为与第一组标签中的标签语义相关，将得分分配给第二组标签中的每个标签，并且基于 在第二组标签中分配给每个标签的分数，将第二组标签中的一个或多个标签分配给图像。
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公开(公告)号：US10055493B2
公开(公告)日：2018-08-21
申请号：US13103445
申请日：2011-05-09
申请人： Geremy A. Heitz, III , Adam Berenzweig , Jason E. Weston , Ron J. Weiss , Sally A. Goldman , Thomas Walters , Samy Bengio , Douglas Eck , Jay M. Ponte , Ryan M. Rifkin
发明人： Geremy A. Heitz, III , Adam Berenzweig , Jason E. Weston , Ron J. Weiss , Sally A. Goldman , Thomas Walters , Samy Bengio , Douglas Eck , Jay M. Ponte , Ryan M. Rifkin
IPC分类号： G06F17/30
CPC分类号： G06F16/639 , G06F16/683
摘要： Generating a playlist may include designating a seed track in an audio library; identifying audio tracks in the audio library having constructs that are within a range of a corresponding construct of the seed track, where the constructs for the audio tracks are derived from frequency representations of the audio tracks, and the corresponding construct for the seed track is derived from a frequency representation of the seed track; and generating the playlist using at least some of the audio tracks that were identified.
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公开(公告)号：US20120082371A1
公开(公告)日：2012-04-05
申请号：US12896318
申请日：2010-10-01
申请人： Samy Bengio , Jason E. Weston
发明人： Samy Bengio , Jason E. Weston
IPC分类号： G06K9/62
CPC分类号： G06K9/6282
摘要： Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for label embedding trees for large multi-class tasks. In one aspect, a method includes mapping each image in a plurality of images and each label in a plurality of labels into a multi-dimensional label embedding space. A tree of label predictors is trained with the plurality of mapped images such that an error function is minimized in which the error function counts an error for each mapped image if any of the label predictors at any depth of the tree incorrectly predicts that the mapped image belongs to the label predictor's respective label set.
摘要翻译： 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，用于为大型多类任务标签嵌入树。 一方面，一种方法包括将多个图像中的每个图像和多个标签中的每个标签映射成多维标签嵌入空间。 使用多个映射图像训练标签预测器的树，使得误差函数被最小化，其中如果在树的任何深度处的任何标签预测器错误地预测了映射图像，则误差函数计算每个映射图像的误差 属于标签预测器的相应标签集。
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公开(公告)号：US20120290621A1
公开(公告)日：2012-11-15
申请号：US13103445
申请日：2011-05-09
申请人： Geremy A. Heitz, III , Adam Berenzweig , Jason E. Weston , Ron J. Weiss , Sally A. Goldman , Thomas Walters , Samy Bengio , Douglas Eck , Jay M. Ponte , Ryan M. Rifkin
发明人： Geremy A. Heitz, III , Adam Berenzweig , Jason E. Weston , Ron J. Weiss , Sally A. Goldman , Thomas Walters , Samy Bengio , Douglas Eck , Jay M. Ponte , Ryan M. Rifkin
IPC分类号： G06F17/30
CPC分类号： G06F17/30772 , G06F17/30743
摘要： Generating a playlist may include designating a seed track in an audio library; identifying audio tracks in the audio library having constructs that are within a range of a corresponding construct of the seed track, where the constructs for the audio tracks are derived from frequency representations of the audio tracks, and the corresponding construct for the seed track is derived from a frequency representation of the seed track; and generating the playlist using at least some of the audio tracks that were identified.
摘要翻译： 生成播放列表可以包括在音频库中指定种子轨道; 识别具有在种子轨道的相应构造的范围内的构造的音频库中的音轨，其中音频轨道的构造从音频轨道的频率表示导出，并且导出用于种子轨道的相应构造 从种子轨迹的频率表示; 以及使用所识别的至少一些音轨生成播放列表。
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公开(公告)号：US08855430B1
公开(公告)日：2014-10-07
申请号：US13527783
申请日：2012-06-20
申请人： Neil G. Alldrin , Charles J. Rosenberg , Bin Shen , Samy Bengio , Zhen Hao Zhou
发明人： Neil G. Alldrin , Charles J. Rosenberg , Bin Shen , Samy Bengio , Zhen Hao Zhou
IPC分类号： G06K9/62
CPC分类号： G06F17/30268 , G06K9/00664
摘要： 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.
摘要翻译： 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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