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公开(公告)号：US08873838B2
公开(公告)日：2014-10-28
申请号：US13804257
申请日：2013-03-14
Applicant: Google Inc.
Inventor： Mustafa Suleyman , Benjamin Kenneth Coppin , Marek Barwinski , Arun Nair , Andrei-Alexandru Rusu , Chia-Yueh Carlton Chu
IPC: G06K9/62
CPC classification number: G06K9/6217 , G06K9/00362 , G06K9/4628 , G06N3/0454 , G06N3/084
Abstract: The present invention relates to a method and system for characterizing an image. The characterization may then be used to conduct a search for similar images, for example using a learning system trained using previously characterized images. A face may be identified within the image and a subsection extracted from said image which does not contain said face. At least one fixed size patch is taken from said extracted subsection; and input into said learning network to characterize said image.
Abstract translation: 本发明涉及一种用于表征图像的方法和系统。 然后，可以使用表征来进行类似图像的搜索，例如使用使用先前描述的图像训练的学习系统。 可以在图像内识别面部和从不包含所述脸部的所述图像提取的子部分。 从所述提取的子段中取出至少一个固定大小的补丁; 并输入到所述学习网络中以表征所述图像。
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公开(公告)号：US08971669B2
公开(公告)日：2015-03-03
申请号：US14155434
申请日：2014-01-15
Applicant: Google Inc.
Inventor： Benjamin Kenneth Coppin , Mustafa Suleyman , Arun Nair
CPC classification number: G06F17/30262 , G06K9/00 , G06K9/46 , G06K2009/4666
Abstract: A method for processing an image to generate a signature which is characteristic of a pattern within said image. The method comprising receiving an image; overlaying a window at multiple locations on said image to define a plurality of sub-images within said image, with each sub-image each having a plurality of pixels having a luminance level; determining a luminance value for each said sub-image, wherein said luminance value is derived from said luminance levels of said plurality of pixels; and combining said luminance values for each of said sub-images to form said signature. Said combining is such that said signature is independent of the location of each sub-image. A method of creating a database of images using said method of generating signatures is also described.
Abstract translation: 一种用于处理图像以生成作为所述图像内的图案的特征的签名的方法。 该方法包括接收图像; 在所述图像上的多个位置处覆盖窗口以在所述图像内定义多个子图像，每个子图像各自具有具有亮度级的多个像素; 确定每个所述子图像的亮度值，其中所述亮度值从所述多个像素的所述亮度级导出; 以及将每个所述子图像的所述亮度值组合以形成所述签名。 所述组合使得所述签名独立于每个子图像的位置。 还描述了使用所述生成签名的方法来创建图像数据库的方法。
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公开(公告)号：US20140270488A1
公开(公告)日：2014-09-18
申请号：US13804257
申请日：2013-03-14
Applicant: Google Inc.
Inventor： Mustafa Suleyman , Benjamin Kenneth Coppin , Marek Barwinski , Arun Nair , Andrei-Alexandru Rusu , Chia-Yueh Carlton Chu
IPC: G06K9/62
CPC classification number: G06K9/6217 , G06K9/00362 , G06K9/4628 , G06N3/0454 , G06N3/084
Abstract: The present invention relates to a method and system for characterizing an image. The characterization may then be used to conduct a search for similar images, for example using a learning system trained using previously characterized images. A face may be identified within the image and a subsection extracted from said image which does not contain said face. At least one fixed size patch is taken from said extracted subsection; and input into said learning network to characterize said image.
Abstract translation: 本发明涉及一种用于表征图像的方法和系统。 然后，可以使用表征来进行类似图像的搜索，例如使用使用先前描述的图像训练的学习系统。 可以在图像内识别面部和从不包含所述脸部的所述图像提取的子部分。 从所述提取的子段中取出至少一个固定大小的补丁; 并输入到所述学习网络中以表征所述图像。
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公开(公告)号：US20180018580A1
公开(公告)日：2018-01-18
申请号：US15212037
申请日：2016-07-15
Applicant: Google Inc.
Inventor： Benjamin Kenneth Coppin , Mustafa Suleyman , Thomas Chadwick Walters , Timothy Mann , Chia-Yueh Carlton Chu , Martin Szummer , Luis Carlos Cobo Rus , Jean-Francois Crespo
CPC classification number: G06N20/00 , G06F16/24578 , G06F16/9535 , G06N3/0445 , G06N3/084 , G06N7/005
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using a machine learning model that has been trained through reinforcement learning to select a content item. One of the methods includes receiving first data characterizing a first context in which a first content item may be presented to a first user in a presentation environment; and providing the first data as input to a long-term engagement machine learning model, the model having been trained through reinforcement learning to: receive a plurality of inputs, and process each of the plurality of inputs to generate a respective engagement score for each input that represents a predicted, time-adjusted total number of selections by the respective user of future content items presented to the respective user in the presentation environment if the respective content item is presented in the respective context.
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