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公开(公告)号：US09330171B1
公开(公告)日：2016-05-03
申请号：US14161146
申请日：2014-01-22
Applicant: Google Inc.
Inventor： Sanketh Shetty , Andrej Karpathy , George Dan Toderici
CPC classification number: G06F17/30784 , G06F17/30247 , G06K9/00744 , G06K9/64 , G06T3/00
Abstract: A method includes receiving, by a processing device of a content sharing platform, a video content, selecting at least one video frame from the video content, subsampling the at least one video frame to generate a first representation of the at least one video frame, selecting a sub-region of the at least one video frame to generate a second representation of the at least one video frame, and applying a convolutional neuron network to the first and second representations of the at least one video frame to generate an annotation for the video content.
Abstract translation: 一种方法包括：通过内容共享平台的处理设备接收视频内容，从视频内容中选择至少一个视频帧，对所述至少一个视频帧进行二次采样以生成所述至少一个视频帧的第一表示， 选择所述至少一个视频帧的子区域以生成所述至少一个视频帧的第二表示，以及将卷积神经网络应用于所述至少一个视频帧的所述第一和第二表示，以生成所述至少一个视频帧的注释 视频内容
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公开(公告)号：US09830361B1
公开(公告)日：2017-11-28
申请号：US14096950
申请日：2013-12-04
Applicant: Google Inc.
Inventor： Balakrishnan Varadarajan , George Dan Toderici , Apostol Natsev , Weilong Yang , John Burge , Sanketh Shetty , Omid Madani
CPC classification number: G06F17/3053 , G06F17/241 , G06F17/278 , G06F17/30598
Abstract: Facilitation of content entity annotation while maintaining joint quality, coverage and/or completeness performance conditions is provided. In one example, a system includes an aggregation component that aggregates signals indicative of initial entities for content and initial scores associated with the initial entities generated by one or more content annotation sources; and a mapping component that maps the initial scores to calibrated scores within a defined range. The system also includes a linear aggregation component that: applies selected weights to the calibrated scores, wherein the selected weights are based on joint performance conditions; and combines the weighted, calibrated scores based on a selected linear aggregation model of a plurality of linear aggregation models to generate a final score. The system also includes an annotation component that determines whether to annotate the content with one of the initial entities based on a comparison of the final score and a defined threshold value.
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公开(公告)号：US09779304B2
公开(公告)日：2017-10-03
申请号：US14823946
申请日：2015-08-11
Applicant: Google Inc.
Inventor： Balakrishnan Varadarajan , George Dan Toderici , Apostol Natsev , Nitin Khandelwal , Sudheendra Vijayanarasimhan , Weilong Yang , Sanketh Shetty
CPC classification number: G06K9/00718 , G06F17/30784 , G06F17/3082 , G06K9/52 , G06K9/6201 , G06K9/6256 , G06K9/627 , H04N5/265
Abstract: A system and methodology provide for annotating videos with entities and associated probabilities of existence of the entities within video frames. A computer-implemented method identifies an entity from a plurality of entities identifying characteristics of video items. The computer-implemented method selects a set of features correlated with the entity based on a value of a feature of a plurality of features, determines a classifier for the entity using the set of features, and determines an aggregation calibration function for the entity based on the set of features. The computer-implemented method selects a video frame from a video item, where the video frame having associated features, and determines a probability of existence of the entity based on the associated features using the classifier and the aggregation calibration function.
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公开(公告)号：US20160132771A1
公开(公告)日：2016-05-12
申请号：US14539392
申请日：2014-11-12
Applicant: Google Inc.
Inventor： Sanketh Shetty , Ibrahim Elbouchikhi
CPC classification number: H04L67/32 , G06Q30/0631 , H04W4/60
Abstract: A machine learning technique may be applied to applications hosted by an application store to extract features that can be utilized to train one or more classifiers of the applications based on their relative complexity. A processor may receive pairwise comparisons of relative complexity and feature representations for the applications to be used in training of a classifier. The processor may determine a feature set that is correlated with the pairwise comparison of relative complexity and obtain a classifier based thereupon.
Abstract translation: 机器学习技术可以应用于由应用商店托管的应用，以提取可以用于基于它们的相对复杂度来训练应用的一个或多个分类器的特征。 处理器可以接收用于在分类器的训练中使用的应用的相对复杂性和特征表示的成对比较。 处理器可以确定与相对复杂度的成对比较相关的特征集合，并且基于此获得基于分类器。
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