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公开(公告)号：US20160070962A1
公开(公告)日：2016-03-10
申请号：US14848216
申请日：2015-09-08
Applicant: Google Inc.
Inventor： Sanketh Shetty , Tomas Izo , Min-Hsuan Tsai , Sudheendra Vijayanarasimhan , Apostol Natsev , Sami Abu-El-Haija , George Dan Toderici , Susanna Ricco , Balakrishnan Varadarajan , Nicola Muscettola , WeiHsin Gu , Weilong Yang , Nitin Khandelwal , Phuong Le
CPC classification number: G06K9/00718 , G06F17/30787 , G06K9/00744 , G06K9/00751 , G06K9/00765 , G06K2209/27
Abstract: A computer-implemented method for selecting representative frames for videos is provided. The method includes receiving a video and identifying a set of features for each of the frames of the video. The features including frame-based features and semantic features. The semantic features identifying likelihoods of semantic concepts being present as content in the frames of the video. A set of video segments for the video is subsequently generated. Each video segment includes a chronological subset of frames from the video and each frame is associated with at least one of the semantic features. The method generates a score for each frame of the subset of frames for each video segment based at least on the semantic features, and selecting a representative frame for each video segment based on the scores of the frames in the video segment. The representative frame represents and summarizes the video segment.
Abstract translation: 提供了一种用于选择视频的代表性帧的计算机实现的方法。 该方法包括接收视频并识别视频的每个帧的一组特征。 特征包括基于帧的特征和语义特征。 识别语义概念的可能性的语义特征作为视频帧中的内容呈现。 随后生成视频的一组视频段。 每个视频段包括来自视频的帧的按时间顺序的子集，并且每个帧与语义特征中的至少一个相关联。 该方法至少基于语义特征为每个视频段的帧子集的每帧生成分数，并且基于视频段中的帧的分数为每个视频段选择代表性的帧。 代表性的框架代表和总结视频段。
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Patent Agency Ranking
公开(公告)号：US20170046573A1
公开(公告)日：2017-02-16
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.
Abstract translation: 系统和方法提供用实体注释视频和在视频帧内存在实体的相关概率。 计算机实现的方法从识别视频项目的特征的多个实体中识别实体。 计算机实现的方法基于多个特征的特征的值来选择与实体相关的一组特征，使用该特征集来确定实体的分类器，并且基于该特征确定该实体的聚合校准功能 的功能集。 计算机实现的方法从视频项目中选择视频帧，其中具有相关联特征的视频帧，并且使用分类器和聚合校准功能基于相关联的特征来确定实体的存在概率。
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公开(公告)号：US20160378863A1
公开(公告)日：2016-12-29
申请号：US14749436
申请日：2015-06-24
Applicant: Google Inc.
Inventor： Jonathon Shlens , George Dan Toderici , Sami Ahmad Abu-El-Haija
CPC classification number: G06F16/739 , G06F16/783 , G06K9/00751 , G06N3/0454 , G06N3/08
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting representative frames for videos. One of the methods includes receiving a search query; determining a query representation for the search query; obtaining data identifying a plurality of responsive videos for the search query, wherein each responsive video comprises a plurality of frames, wherein each frame has a respective frame representation; selecting, for each responsive video, a representative frame from the responsive video using the query representation and the frame representations for the frames in the responsive video; and generating a response to the search query, wherein the response to the search query includes a respective video search result for each of the responsive videos, and wherein the respective video search result for each of the responsive videos includes a presentation of the representative video frame from the responsive video.
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，用于为视频选择代表性帧。 其中一种方法包括接收搜索查询; 确定搜索查询的查询表示; 获取识别搜索查询的多个响应视频的数据，其中每个响应视频包括多个帧，其中每个帧具有相应的帧表示; 对于每个响应视频，使用查询表示和响应视频中的帧的帧表示从响应视频中选择代表帧; 以及产生对所述搜索查询的响应，其中对所述搜索查询的所述响应包括每个所述响应视频的相应视频搜索结果，并且其中每个响应视频的相应视频搜索结果包括所述代表性视频帧的呈现 从响应视频。
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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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