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公开(公告)号：US09135674B1
公开(公告)日：2015-09-15
申请号：US14092515
申请日：2013-11-27
Applicant: Google Inc.
Inventor： Jay Yagnik , Henry Rowley , Sergey Ioffe
CPC classification number: G06T1/0021 , G06F17/30784 , G06F17/30787 , G06K9/00744 , G06K9/00758
Abstract: A method and system generates and compares fingerprints for videos in a video library. The video fingerprints provide a compact representation of the temporal locations of discontinuities in the video that can be used to quickly and efficiently identify video content. Discontinuities can be, for example, shot boundaries in the video frame sequence or silent points in the audio stream. Because the fingerprints are based on structural discontinuity characteristics rather than exact bit sequences, visual content of videos can be effectively compared even when there are small differences between the videos in compression factors, source resolutions, start and stop times, frame rates, and so on. Comparison of video fingerprints can be used, for example, to search for and remove copyright protected videos from a video library. Furthermore, duplicate videos can be detected and discarded in order to preserve storage space.
Abstract translation: 方法和系统生成并比较视频库中视频的指纹。 视频指纹提供了可以用于快速和有效地识别视频内容的视频中的不连续性的时间位置的紧凑表示。 不连续可以是例如视频帧序列中的镜头边界或音频流中的无声点。 因为指纹是基于结构不连续特征而不是精确的比特序列，所以即使在压缩因素，源分辨率，开始和停止时间，帧率等之间的视频之间存在小的差异，也可以有效地比较视频的视觉内容 。 可以使用比较视频指纹，例如，从视频库搜索和删除受版权保护的视频。 此外，为了保存存储空间，可以检测和丢弃重复的视频。
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公开(公告)号：US20150186793A1
公开(公告)日：2015-07-02
申请号：US14141803
申请日：2013-12-27
Applicant: GOOGLE INC.
Inventor： Sergey Ioffe , Samy Bengio
CPC classification number: G06N20/00
Abstract: A computer-implemented method can include receiving training data that includes a set of non-matching pairs and a set of matching pairs. The method can further include calculating a non-matching collision probability for each non-matching pair of the set of non-matching pairs and a matching collision probability for each matching pair of the set of matching pairs. The method can also include generating a machine learning model that includes a first threshold and a second threshold. An unknown item and a particular known item are classified as not matching when their collision probability is less than the first threshold, and as matching when their collision probability is greater than the second threshold. The first threshold and the second threshold can be selected based on a minimization of errors in classification of matching and non-matching pairs in the training data, and a maximization of a retrieval efficiency metric.
Abstract translation: 计算机实现的方法可以包括接收包括一组非匹配对和一组匹配对的训练数据。 所述方法还可以包括：计算所述一组非匹配对中的每个非匹配对的不匹配冲突概率以及所述一组匹配对中的每个匹配对的匹配冲突概率。 该方法还可以包括生成包括第一阈值和第二阈值的机器学习模型。 未知项目和特定已知项目当其冲突概率小于第一阈值时被分类为不匹配，并且当其冲突概率大于第二阈值时被匹配。 可以基于训练数据中的匹配和非匹配对的分类中的误差的最小化以及检索效率度量的最大化来选择第一阈值和第二阈值。
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公开(公告)号：US09955196B2
公开(公告)日：2018-04-24
申请号：US14853411
申请日：2015-09-14
Applicant: Google Inc.
Inventor： Sergey Ioffe
IPC: H04N7/173 , H04N21/2343 , H04N21/234 , H04N21/233 , H04N21/2743 , H04N21/8355 , H04N21/845
CPC classification number: H04N21/23439 , H04N21/2335 , H04N21/23418 , H04N21/234345 , H04N21/2743 , H04N21/8355 , H04N21/8456
Abstract: A video server receives an uploaded video and determines whether the video contains third-party content and which portions of the uploaded video match third-party content. The video server determines whether to degrade the matching portions and/or how (e.g., extent, type) to do so. The video server separates the matching portion from original portions in the uploaded video and generates a degraded version of the matching content by applying an effect such as compression, edge distortion, temporal distortion, noise addition, color distortion, or audio distortion. The video server combines the degraded portions with the original portions to output a degraded version of the uploaded video. The video server stores and/or distributes the degraded version of the uploaded video. The video server may offer the uploading user licensing terms with the content owner that the user may accept to reverse the degradation.
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公开(公告)号：US09400809B2
公开(公告)日：2016-07-26
申请号：US14265546
申请日：2014-04-30
Applicant: Google Inc.
Inventor： Sergey Ioffe
IPC: G06F17/30
CPC classification number: G06F17/30277 , G06F17/30247
Abstract: A method and apparatus are provided for performing an image search based on a search query having a portion P1 and a portion P2. Based on the first search query, a second search query is generated that includes a portion P3 and the portion P2 such that the second search query is broader in scope than the first search query, while still retaining the portion P2 of the first query. A first image search is then performed for the first search query to obtain a first set of search results and a second image search is performed for the second search query to obtain a second set of search results. Consequently, an image from the first set of search results is selected for presentation to a user, wherein the selection is based on content of the second set of search results.
Abstract translation: 提供了一种基于具有部分P1和部分P2的搜索查询执行图像搜索的方法和装置。 基于第一搜索查询，生成包括部分P3和部分P2的第二搜索查询，使得第二搜索查询在范围上比第一搜索查询更宽，同时仍保留第一查询的部分P2。 然后对第一搜索查询执行第一图像搜索以获得第一组搜索结果，并且为第二搜索查询执行第二图像搜索以获得第二组搜索结果。 因此，选择来自第一组搜索结果的图像以呈现给用户，其中所述选择基于第二组搜索结果的内容。
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公开(公告)号：US09143784B2
公开(公告)日：2015-09-22
申请号：US14025768
申请日：2013-09-12
Applicant: Google Inc.
Inventor： Jay Yagnik , Sergey Ioffe
CPC classification number: H04N19/60 , G06K9/00744
Abstract: This disclosure relates to transformation invariant media matching. A fingerprinting component can generate a transformation invariant identifier for media content by adaptively encoding the relative ordering of signal markers in media content. The signal markers can be adaptively encoded via reference point geometry, or ratio histograms. An identification component compares the identifier against a set of identifiers for known media content, and the media content can be matched or identified as a function of the comparison.
Abstract translation: 本公开涉及变换不变媒体匹配。 指纹分量可以通过对媒体内容中的信号标记的相对排序进行自适应编码来生成媒体内容的变换不变标识符。 信号标记可以通过参考点几何或比例直方图进行自适应编码。 识别部件将标识符与已知媒体内容的一组标识符进行比较，并且媒体内容可以作为比较的函数进行匹配或标识。
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Patent Agency Ranking
公开(公告)号：US09063954B2
公开(公告)日：2015-06-23
申请号：US13832122
申请日：2013-03-15
Applicant: Google Inc.
Inventor： Sergey Ioffe , Mohamed Aly , Charles J. Rosenberg
CPC classification number: G06F17/30247 , G06F17/3025 , G06K9/4676 , G06K9/6202
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining image search results. One of the methods includes generating a plurality of feature vectors for each image in a collection of images, wherein each feature vector is associated with an image tile of an image, wherein each feature vector corresponds to one of a plurality of predetermined visual words. All images in the collection of images that share at least a threshold number of matching visual words associated with matching image tiles are classified as near-duplicate images.
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，用于确定图像搜索结果。 一种方法包括为图像集合中的每个图像生成多个特征向量，其中每个特征向量与图像的图像块相关联，其中每个特征向量对应于多个预定视觉词中的一个。 共享与匹配的图像块相关联的至少阈值数量的匹配视觉词的图像集合中的所有图像被分类为近似重复的图像。
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公开(公告)号：US20150169646A1
公开(公告)日：2015-06-18
申请号：US14265546
申请日：2014-04-30
Applicant: Google Inc.
Inventor： Sergey Ioffe
IPC: G06F17/30
CPC classification number: G06F17/30277 , G06F17/30247
Abstract: A method and apparatus are provided for performing an image search based on a search query having a portion P1 and a portion P2. Based on the first search query, a second search query is generated that includes a portion P3 and the portion P2 such that the second search query is broader in scope than the first search query, while still retaining the portion P2 of the first query. A first image search is then performed for the first search query to obtain a first set of search results and a second image search is performed for the second search query to obtain a second set of search results. Consequently, an image from the first set of search results is selected for presentation to a user, wherein the selection is based on content of the second set of search results.
Abstract translation: 提供了一种基于具有部分P1和部分P2的搜索查询执行图像搜索的方法和装置。 基于第一搜索查询，生成包括部分P3和部分P2的第二搜索查询，使得第二搜索查询在范围上比第一搜索查询更宽，同时仍保留第一查询的部分P2。 然后对第一搜索查询执行第一图像搜索以获得第一组搜索结果，并且为第二搜索查询执行第二图像搜索以获得第二组搜索结果。 因此，选择来自第一组搜索结果的图像以呈现给用户，其中所述选择基于第二组搜索结果的内容。
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公开(公告)号：US20140016706A1
公开(公告)日：2014-01-16
申请号：US14025768
申请日：2013-09-12
Applicant: Google Inc.
Inventor： Jay Yagnik , Sergey Ioffe
IPC: H04N7/30
CPC classification number: H04N19/60 , G06K9/00744
Abstract: This disclosure relates to transformation invariant media matching. A fingerprinting component can generate a transformation invariant identifier for media content by adaptively encoding the relative ordering of signal markers in media content. The signal markers can be adaptively encoded via reference point geometry, or ratio histograms. An identification component compares the identifier against a set of identifiers for known media content, and the media content can be matched or identified as a function of the comparison.
Abstract translation: 本公开涉及变换不变媒体匹配。 指纹分量可以通过对媒体内容中的信号标记的相对排序进行自适应编码来生成媒体内容的变换不变标识符。 信号标记可以通过参考点几何或比例直方图进行自适应编码。 识别部件将标识符与已知媒体内容的一组标识符进行比较，并且媒体内容可以作为比较的函数进行匹配或标识。
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公开(公告)号：US10387749B2
公开(公告)日：2019-08-20
申请号：US15710377
申请日：2017-09-20
Applicant: Google Inc.
Inventor： Yair Movshovitz-Attias , King Hong Leung , Saurabh Singh , Alexander Toshev , Sergey Ioffe
Abstract: The present disclosure provides systems and methods that enable distance metric learning using proxies. A machine-learned distance model can be trained in a proxy space in which a loss function compares an embedding provided for an anchor data point of a training dataset to a positive proxy and one or more negative proxies, where each of the positive proxy and the one or more negative proxies serve as a proxy for two or more data points included in the training dataset. Thus, each proxy can approximate a number of data points, enabling faster convergence. According to another aspect, the proxies of the proxy space can themselves be learned parameters, such that the proxies and the model are trained jointly. Thus, the present disclosure enables faster convergence (e.g., reduced training time). The present disclosure provides example experiments which demonstrate a new state of the art on several popular training datasets.
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公开(公告)号：US20170243085A1
公开(公告)日：2017-08-24
申请号：US15395530
申请日：2016-12-30
Applicant: Google Inc.
Inventor： Vincent O. Vanhoucke , Christian Szegedy , Sergey Ioffe
CPC classification number: G06K9/6267 , G06K9/00979 , G06K9/4628 , G06N3/04 , G06N3/0445 , G06N3/08
Abstract: A neural network system that includes: multiple subnetworks that includes: a first subnetwork including multiple first modules, each first module including: a pass-through convolutional layer configured to process the subnetwork input for the first subnetwork to generate a pass-through output; an average pooling stack of neural network layers that collectively processes the subnetwork input for the first subnetwork to generate an average pooling output; a first stack of convolutional neural network layers configured to collectively process the subnetwork input for the first subnetwork to generate a first stack output; a second stack of convolutional neural network layers that are configured to collectively process the subnetwork input for the first subnetwork to generate a second stack output; and a concatenation layer configured to concatenate the pass-through output, the average pooling output, the first stack output, and the second stack output to generate a first module output for the first module.
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