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公开(公告)号：US09355067B1
公开(公告)日：2016-05-31
申请号：US14691362
申请日：2015-04-20
Applicant: Google Inc.
Inventor： Rajat Monga , Xiaoyun Wu , Andrew Yan-Tak Ng
CPC classification number: G06F17/10 , G06F17/17 , G06F17/18 , G06F17/5009 , G06N7/005 , G06N99/005
Abstract: Systems and methods are disclosed for distributed first- or higher-order model fitting algorithms. Determination of the parameter set for the objective function is divided into a plurality of sub-processes, each performed by one of a plurality of worker computers. A master computer coordinates the operation of the plurality of worker computers, each operating on a portion of the parameter set such that no two worker computers contain exactly the same parameter subset nor the complete parameter set. Each worker computer performs its sub-processes on its parameter subset, together with training data. For maximum efficiency, the sub-processes are performed using a compact set of instruction primitives. The results are evaluated by the master computer, which may coordinate additional sub-process operations to perform higher-order optimization or terminate the optimization method and proceed to formulation of a model function.
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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。
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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。
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