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61. 发明授权

US09524487B1 System and methods for detecting temporal music trends from online services 有权
标题翻译：用于从在线服务中检测时间音乐趋势的系统和方法
公开(公告)号：US09524487B1
公开(公告)日：2016-12-20
申请号：US13466817
申请日：2012-05-08
申请人： Jay Yagnik , Douglas Eck
发明人： Jay Yagnik , Douglas Eck
IPC分类号： G06F15/16 , G06Q10/10 , G06F17/30 , G06Q30/00
CPC分类号： G06Q10/10 , G06F17/30 , G06Q30/00
摘要： A system and methods for automatically detecting temporal music trends by observing music consumption by users of online services, for example, social networks, and user sharing habits. In some embodiments, the system and methods gather music consumption patterns (e.g., downloading, listening, sharing or the like) of users, including music identifiers for a track, album, or playlist in a user's music library and time stamps that indicate consumption times corresponding to the music identifiers. A temporal trends detection engine determines music of interest to users by analyzing music consumption patterns of users, user interests and tastes in music, and social affinity between users. A recommendations engine automatically generates and transmits recommendations of music determined by the temporal trends detection engine to be of interest to users.
摘要翻译：用于通过观察在线服务的用户（例如，社交网络）和用户共享习惯的音乐消费来自动检测时间音乐趋势的系统和方法。在一些实施例中，系统和方法收集用户的音乐消费模式（例如，下载，收听，共享等），包括用户音乐库中的音轨，专辑或播放列表的音乐标识符，以及指示消费时间的时间戳对应于音乐标识符。时间趋势检测引擎通过分析用户的音乐消费模式，音乐中的用户兴趣和品味以及用户之间的社交关系来确定用户感兴趣的音乐。推荐引擎自动生成并发送由时间趋势检测引擎确定的用户兴趣的音乐推荐。

62. 发明授权

US09483701B1 System and method for using segmentation to identify object location in images 有权
标题翻译：使用分割来识别图像中的对象位置的系统和方法
公开(公告)号：US09483701B1
公开(公告)日：2016-11-01
申请号：US13299281
申请日：2011-11-17
申请人： Vivek Kwatra , Jay Yagnik , Alexander T. Toshev
发明人： Vivek Kwatra , Jay Yagnik , Alexander T. Toshev
IPC分类号： G06K9/62 , G06K9/00 , G06K9/32
CPC分类号： G06K9/3241 , G06K9/00771 , G06K9/468 , G06K9/6255 , G06T7/0081 , G06T7/0097 , G06T2207/20081
摘要： A computing device segments an image into a plurality of segments, wherein each segment of the plurality of segments has a segment location and a set of pixels that share visual characteristics. The computing device determines an initial set of bounding boxes for the image based on the plurality of segments. The computing device determines a reduced set of bounding boxes based on combining bounding boxes of the initial set of bounding boxes, the reduced set of bounding boxes corresponding to one or more objects in the image, each of the one or more objects having an object class and an object location.
摘要翻译：计算设备将图像分割成多个段，其中多个段中的每个段具有共享视觉特征的段位置和一组像素。计算设备基于多个段确定图像的初始边界框集合。计算设备基于组合边界框的边界框来确定缩小的边界框集合，所述边界框对应于所述图像中的一个或多个对象，所述一个或多个对象中的每一个具有对象类和对象位置。

63. 发明授权

US09158842B1 Sound representation via winner-take-all coding of auditory spectra 有权
标题翻译：通过听觉光谱的获胜者全部编码的声音表示
公开(公告)号：US09158842B1
公开(公告)日：2015-10-13
申请号：US13616938
申请日：2012-09-14
申请人： Jay Yagnik , Richard Francis Lyon , Thomas Chadwick Walters , Douglas Eck
发明人： Jay Yagnik , Richard Francis Lyon , Thomas Chadwick Walters , Douglas Eck
IPC分类号： G06F17/30
CPC分类号： G06F17/30743 , G06F17/00 , G06F17/3074
摘要： Sound representations and winner-take-all codes of auditory spectra are used in the identification of audio content. A transformation component converts a set of sound frames from audio content into a set of spectral slices. A spectral encoder component encodes the spectral slices of auditory spectra into winner-take-all codes with a winner-take-all hash function. An identification component identifies which spectral dimension of a subset of spectral dimensions within a spectral slice has highest spectral value according to the winner-take-all codes. Reference audio content is determined to be similar or matching to the audio content based on the winner-take-all codes.
摘要翻译：听觉谱中的声音表示和获胜者代码都用于音频内容的识别。变换分量将一组声音帧从音频内容转换成一组光谱片段。频谱编码器组件将听觉光谱的频谱切片编成具有获胜者全部散列函数的获胜者所有代码。识别部件根据获胜者全部代码识别光谱切片内的光谱维度的子集的哪个光谱尺寸具有最高光谱值。参考音频内容被确定为基于获胜者所有代码与音频内容相似或匹配。

64. 发明授权

US08977374B1 Geometric and acoustic joint learning 有权
标题翻译：几何和声学联合学习
公开(公告)号：US08977374B1
公开(公告)日：2015-03-10
申请号：US13610866
申请日：2012-09-12
申请人： Douglas Eck , Jay Yagnik
发明人： Douglas Eck , Jay Yagnik
IPC分类号： G06F17/00 , G06F17/30 , G06F7/00 , G06G7/12 , H04H60/65 , H04H60/58 , G11B27/30 , H04H60/37 , H04H60/47 , H04H60/48
CPC分类号： H04H60/65 , G06F17/30778 , G10L25/63 , G11B27/105 , G11B27/28 , G11B27/3009 , H04H60/37 , H04H60/47 , H04H60/48 , H04H60/58
摘要： Described herein are methods and system for analyzing music audio. An example method includes obtaining a music audio track, calculating acoustic features of the music audio track, calculating geometric features of the music audio track in view of the acoustic features, and determining a mood of the music audio track in view of the geometric features.
摘要翻译：这里描述了用于分析音乐音频的方法和系统。示例性方法包括：获得音乐音轨，计算音乐音轨的声学特征，根据声学特征计算音乐音轨的几何特征，以及根据几何特征确定音乐音轨的情绪。

65. 发明授权

US08768048B1 System and method for exploiting segment co-occurrence relationships to identify object location in images 有权
标题翻译：利用片段同现关系识别图像中对象位置的系统和方法
公开(公告)号：US08768048B1
公开(公告)日：2014-07-01
申请号：US13299789
申请日：2011-11-18
申请人： Vivek Kwatra , Jay Yagnik , Alexander T. Toshev , Poonam Suryanarayan
发明人： Vivek Kwatra , Jay Yagnik , Alexander T. Toshev , Poonam Suryanarayan
IPC分类号： G06K9/62
CPC分类号： G06K9/6202 , G06K9/72
摘要： A computing device segments an image into a plurality of segments, wherein each segment of the plurality of segments comprises a set of pixels that share visual characteristics. The computing device then determines expected contexts for the segments, wherein an expected context for a segment comprises at least one of additional segments or features expected to occur in the image together with the segment. The computing device then identifies a probable object based on the expected contexts.
摘要翻译：计算设备将图像分割成多个段，其中多个段中的每个段包括共享视觉特征的一组像素。计算设备然后确定段的预期上下文，其中段的预期上下文包括期望与该段一起出现在图像中的附加段或特征中的至少一个。然后，计算设备基于预期上下文识别可能的对象。

66. 发明授权

US08738633B1 Transformation invariant media matching 有权
标题翻译：转换不变媒体匹配
公开(公告)号：US08738633B1
公开(公告)日：2014-05-27
申请号：US13362905
申请日：2012-01-31
申请人： Matthew Sharifi , Sergey Ioffe , Jay Yagnik , Gheorghe Postelnicu , Dominik Roblek , George Tzanetakis
发明人： Matthew Sharifi , Sergey Ioffe , Jay Yagnik , Gheorghe Postelnicu , Dominik Roblek , George Tzanetakis
IPC分类号： G06F17/30
CPC分类号： G06K9/6267 , G06F17/3002 , G06F17/30244 , G06K9/00013
摘要： 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 interest points in media content. The interest points can be grouped into subsets, and stretch invariant descriptors can be generated for the subsets based on ratios of coordinates of interest points included in the subsets. The stretch invariant descriptors can be aggregated into a transformation invariant identifier. 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.
摘要翻译：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兴趣点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。可以将兴趣点分组为子集，并且可以基于子集中包括的兴趣点坐标的比例为子集生成拉伸不变描述符。拉伸不变描述符可以聚合成变换不变标识符。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。

67. 发明授权

US08676725B1 Method and system for entropy-based semantic hashing 有权
标题翻译：基于熵的语义散列的方法和系统
公开(公告)号：US08676725B1
公开(公告)日：2014-03-18
申请号：US12794380
申请日：2010-06-04
申请人： Ruei-Sung Lin , David Ross , Jay Yagnik
发明人： Ruei-Sung Lin , David Ross , Jay Yagnik
IPC分类号： G06F15/18
CPC分类号： G06N99/005
摘要： Methods, systems and articles of manufacture for identifying semantic nearest neighbors in a feature space are described herein. A method embodiment includes generating an affinity matrix for objects in a given feature space, wherein the affinity matrix identifies the semantic similarity between each pair of objects in the feature space, training a multi-bit hash function using a greedy algorithm that increases the Hamming distance between dissimilar objects in the feature space while minimizing the Hamming distance between similar objects, and identifying semantic nearest neighbors for an object in a second feature space using the multi-bit hash function. A system embodiment includes a hash generator configured to generate the affinity matrix and train the multi-bit hash function, and a similarity determiner configured to identify semantic nearest neighbors for an object in a second feature space using the multi-bit hash function.
摘要翻译：本文描述了用于识别特征空间中的语义最近邻居的方法，系统和制品。方法实施例包括为给定特征空间中的对象生成亲和度矩阵，其中亲和矩阵识别特征空间中每对对象之间的语义相似性，使用增加汉明距离的贪心算法训练多比特哈希函数在特征空间中的不相似对象之间，同时使相似对象之间的汉明距离最小化，并且使用多位哈希函数来识别第二特征空间中的对象的语义最近邻居。系统实施例包括被配置为生成亲和度矩阵并训练多比特哈希函数的哈希发生器，以及被配置为使用多比特哈希函数来识别第二特征空间中的对象的语义最近邻居的相似性确定器。

68. 发明授权

US08589457B1 Training scoring models optimized for highly-ranked results 有权
公开(公告)号：US08589457B1
公开(公告)日：2013-11-19
申请号：US13616108
申请日：2012-09-14
申请人： Samy Bengio , Gal Chechik , Sergey Ioffe , Jay Yagnik
发明人： Samy Bengio , Gal Chechik , Sergey Ioffe , Jay Yagnik
IPC分类号： G06F17/00
CPC分类号： G06K9/66 , G06F17/30244 , G06F17/3053 , G06K9/6267 , Y10S707/913 , Y10S707/915
摘要： Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training scoring models. One method includes storing data identifying a plurality of positive and a plurality of negative training images for a query. The method further includes selecting a first image from either the positive group of images or the negative group of images, and applying a scoring model to the first image. The method further includes selecting a plurality of candidate images from the other group of images, applying the scoring model to each of the candidate images, and then selecting a second image from the candidate images according to scores for the images. The method further includes determining that the scores for the first image and the second image fail to satisfy a criterion, updating the scoring model, and storing the updated scoring model.

69. 发明授权

US08452778B1 Training of adapted classifiers for video categorization 有权
标题翻译：适应分类器的视频分类培训
公开(公告)号：US08452778B1
公开(公告)日：2013-05-28
申请号：US12874015
申请日：2010-09-01
申请人： Yang Song , Ming Zhao , Jay Yagnik
发明人： Yang Song , Ming Zhao , Jay Yagnik
IPC分类号： G06F17/30 , G10L19/12
CPC分类号： G06F17/30799 , G06F17/30796 , G06K9/00711
摘要： A classifier training system trains adapted classifiers for classifying videos based at least in part on scores produced by application of text-based classifiers to textual metadata of the videos. Each classifier corresponds to a particular category, and when applied to a given video indicates whether the video represents the corresponding category. The classifier training system applies the text-based classifiers to textual metadata of the videos to obtain the scores, and also extracts features from content of the videos, combining the scores and the content features for a video into a set of hybrid features. The adapted classifiers are then trained on the hybrid features. The adaption of the text-based classifiers from the textual domain to the video domain allows the training of accurate video classifiers (the adapted classifiers) without requiring a large training set of authoritatively labeled videos.
摘要翻译：分类器训练系统训练适用的分类器，用于至少部分地基于将基于文本的分类器应用于视频的文本元数据而产生的分数来分类视频。每个分类器对应于特定类别，并且当应用于给定视频时指示视频是否表示相应类别。分类器训练系统将基于文本的分类器应用于视频的文本元数据以获得分数，并且还从视频内容中提取特征，将视频的分数和内容特征组合成一组混合特征。然后对适应的分类器对混合特征进行训练。基于文本的分类器从文本域到视频域的适应允许训练准确的视频分类器（适应的分类器），而不需要大量的授权标签视频的训练集。

70. 发明申请

US20120272259A1 VIDEO CONTENT ANALYSIS FOR AUTOMATIC DEMOGRAPHICS RECOGNITION OF USERS AND VIDEOS 有权
标题翻译：视频内容分析用于自动人脸识别用户和视频
公开(公告)号：US20120272259A1
公开(公告)日：2012-10-25
申请号：US13488126
申请日：2012-06-04
申请人： Corinna Cortes , Sanjiv Kumar , Ameesh Makadia , Gideon Mann , Jay Yagnik , Ming Zhao
发明人： Corinna Cortes , Sanjiv Kumar , Ameesh Makadia , Gideon Mann , Jay Yagnik , Ming Zhao
IPC分类号： H04N21/24
CPC分类号： H04N21/2407 , G06F17/30784 , G06F17/30817 , G06F17/3082 , G06F17/30828 , G06K9/00711 , H04N21/23418 , H04N21/25883 , H04N21/4668 , H04N21/4826
摘要： A video demographics analysis system selects a training set of videos to use to correlate viewer demographics and video content data. The video demographics analysis system extracts demographic data from viewer profiles related to videos in the training set and creates a set of demographic distributions, and also extracts video data from videos in the training set. The video demographics analysis system correlates the viewer demographics with the video data of videos viewed by that viewer. Using the prediction model produced by the machine learning process, a new video about which there is no a priori knowledge can be associated with a predicted demographic distribution specifying probabilities of the video appealing to different types of people within a given demographic category, such as people of different ages within an age demographic category.
摘要翻译：视频人口统计分析系统选择用于将观众人口特征和视频内容数据相关联的一组视频。视频人口统计分析系统从与训练集中的视频相关的观众简档中提取人口统计学数据，并创建一组人口分布，并从训练集中的视频中提取视频数据。视频人口统计分析系统将观众人口统计学与观众观看的视频的视频数据相关联。使用机器学习过程产生的预测模型，可以将预测的人口分布与预测的人口分布相关联，所述预测人口统计分布规定了给定人口统计学类别中的不同类型的人的视频的概率，例如人在不同年龄的人口统计学类别。

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