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1. 发明授权

US08463719B2 Audio classification for information retrieval using sparse features 有权
标题翻译：使用稀疏特征进行信息检索的音频分类
公开(公告)号：US08463719B2
公开(公告)日：2013-06-11
申请号：US12722437
申请日：2010-03-11
申请人： Richard F. Lyon , Martin Rehn , Thomas Walters , Samy Bengio , Gal Chechik
发明人： Richard F. Lyon , Martin Rehn , Thomas Walters , Samy Bengio , Gal Chechik
IPC分类号： G06F15/18
CPC分类号： G10L25/48 , G06F17/30743
摘要： Methods, systems, and apparatus, including computer programs encoded on computer storage media, are provided for using audio features to classify audio for information retrieval. In general, one aspect of the subject matter described in this specification can be embodied in methods that include the actions of generating a collection of auditory images, each auditory image being generated from respective audio files according to an auditory model; extracting sparse features from each auditory image in the collection to generate a sparse feature vector representing the corresponding audio file; and ranking the audio files in response to a query including one or more words using the sparse feature vectors and a matching function relating sparse feature vectors to words in the query.
摘要翻译：提供方法，系统和装置，包括在计算机存储介质上编码的计算机程序，用于使用音频特征来分类用于信息检索的音频。通常，本说明书中描述的主题的一个方面可以包括生成听觉图像的集合的动作的方法，每个听觉图像根据听觉模型从各个音频文件生成; 从集合中的每个听觉图像中提取稀疏特征以生成表示相应音频文件的稀疏特征向量; 以及响应于包括使用所述稀疏特征向量的一个或多个单词的查询和将稀疏特征向量与所述查询中的单词相关联的匹配函数进行排序。

2. 发明申请

US20100257129A1 AUDIO CLASSIFICATION FOR INFORMATION RETRIEVAL USING SPARSE FEATURES 有权
标题翻译：使用稀疏特征的信息检索的音频分类
公开(公告)号：US20100257129A1
公开(公告)日：2010-10-07
申请号：US12722437
申请日：2010-03-11
申请人： Robert F. Lyon , Martin Rehn , Thomas Walters , Samy Bengio , Gal Chechik
发明人： Robert F. Lyon , Martin Rehn , Thomas Walters , Samy Bengio , Gal Chechik
IPC分类号： G06F17/30 , G06F15/18
CPC分类号： G10L25/48 , G06F17/30743
摘要： Methods, systems, and apparatus, including computer programs encoded on computer storage media, are provided for using audio features to classify audio for information retrieval. In general, one aspect of the subject matter described in this specification can be embodied in methods that include the actions of generating a collection of auditory images, each auditory image being generated from respective audio files according to an auditory model; extracting sparse features from each auditory image in the collection to generate a sparse feature vector representing the corresponding audio file; and ranking the audio files in response to a query including one or more words using the sparse feature vectors and a matching function relating sparse feature vectors to words in the query.
摘要翻译：提供方法，系统和装置，包括在计算机存储介质上编码的计算机程序，用于使用音频特征来分类用于信息检索的音频。通常，本说明书中描述的主题的一个方面可以包括生成听觉图像的集合的动作的方法，每个听觉图像根据听觉模型从各个音频文件生成; 从集合中的每个听觉图像中提取稀疏特征以生成表示相应音频文件的稀疏特征向量; 以及响应于包括使用所述稀疏特征向量的一个或多个单词的查询和将稀疏特征向量与查询中的单词相关联的匹配函数进行排序。

3. 发明授权

US08429212B1 Training scoring models optimized for highly-ranked results 失效
公开(公告)号：US08429212B1
公开(公告)日：2013-04-23
申请号：US13342532
申请日：2012-01-03
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.

4. 发明授权

US08131786B1 Training scoring models optimized for highly-ranked results 有权
标题翻译：培训评分模型针对高排名结果进行了优化
公开(公告)号：US08131786B1
公开(公告)日：2012-03-06
申请号：US12624001
申请日：2009-11-23
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.
摘要翻译：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，用于训练评分模型。一种方法包括存储识别用于查询的多个正训练图像和多个负训练图像的数据。该方法还包括从图像的正组或负图像组中选择第一图像，以及将评分模型应用于第一图像。该方法还包括从另一组图像中选择多个候选图像，将评分模型应用于每个候选图像，然后根据图像的分数从候选图像中选择第二图像。该方法还包括确定第一图像和第二图像的分数不能满足标准，更新评分模型，并存储更新的评分模型。

5. 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.

6. 发明申请

US20110047163A1 Relevance-Based Image Selection 审中-公开
标题翻译：相关性图像选择
公开(公告)号：US20110047163A1
公开(公告)日：2011-02-24
申请号：US12546436
申请日：2009-08-24
申请人： Gal Chechik , Samy Bengio
发明人： Gal Chechik , Samy Bengio
IPC分类号： G06F17/30
CPC分类号： G06F16/7867 , G06F16/70 , G06F16/738 , G06F16/743 , G06F16/78 , G06F16/783
摘要： A system, computer readable storage medium, and computer-implemented method presents video search results responsive to a user keyword query. The video hosting system uses a machine learning process to learn a feature-keyword model associating features of media content from a labeled training dataset with keywords descriptive of their content. The system uses the learned model to provide video search results relevant to a keyword query based on features found in the videos. Furthermore, the system determines and presents one or more thumbnail images representative of the video using the learned model.
摘要翻译：系统，计算机可读存储介质和计算机实现的方法响应于用户关键词查询呈现视频搜索结果。视频托管系统使用机器学习过程来学习将标记训练数据集中的媒体内容的特征与描述其内容的关键字相关联的特征关键字模型。该系统使用学习模型，根据视频中的功能提供与关键字查询相关的视频搜索结果。此外，系统使用所学习的模型来确定并呈现代表视频的一个或多个缩略图。

7. 发明授权

US08429168B1 Learning semantic image similarity 有权
公开(公告)号：US08429168B1
公开(公告)日：2013-04-23
申请号：US12638704
申请日：2009-12-15
申请人： Gal Chechik , Samy Bengio , Varun Sharma
发明人： Gal Chechik , Samy Bengio , Varun Sharma
IPC分类号： G06F17/30
CPC分类号： G06K9/66 , G06F17/30247 , G06F17/30265 , G06K9/6212 , G06K9/6276
摘要： Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for identifying similar images. In some implementations, a method is provided that includes receiving a collection of images and data associated with each image in the collection of images; generating a sparse feature representation for each image in the collection of images; and training an image similarity function using image triplets sampled from the collection of images and corresponding sparse feature representations.

8. 发明授权

US10055493B2 Generating a playlist 有权
公开(公告)号：US10055493B2
公开(公告)日：2018-08-21
申请号：US13103445
申请日：2011-05-09
申请人： Geremy A. Heitz, III , Adam Berenzweig , Jason E. Weston , Ron J. Weiss , Sally A. Goldman , Thomas Walters , Samy Bengio , Douglas Eck , Jay M. Ponte , Ryan M. Rifkin
发明人： Geremy A. Heitz, III , Adam Berenzweig , Jason E. Weston , Ron J. Weiss , Sally A. Goldman , Thomas Walters , Samy Bengio , Douglas Eck , Jay M. Ponte , Ryan M. Rifkin
IPC分类号： G06F17/30
CPC分类号： G06F16/639 , G06F16/683
摘要： Generating a playlist may include designating a seed track in an audio library; identifying audio tracks in the audio library having constructs that are within a range of a corresponding construct of the seed track, where the constructs for the audio tracks are derived from frequency representations of the audio tracks, and the corresponding construct for the seed track is derived from a frequency representation of the seed track; and generating the playlist using at least some of the audio tracks that were identified.

9. 发明申请

US20120290621A1 GENERATING A PLAYLIST 审中-公开
标题翻译：生成播放列表
公开(公告)号：US20120290621A1
公开(公告)日：2012-11-15
申请号：US13103445
申请日：2011-05-09
申请人： Geremy A. Heitz, III , Adam Berenzweig , Jason E. Weston , Ron J. Weiss , Sally A. Goldman , Thomas Walters , Samy Bengio , Douglas Eck , Jay M. Ponte , Ryan M. Rifkin
发明人： Geremy A. Heitz, III , Adam Berenzweig , Jason E. Weston , Ron J. Weiss , Sally A. Goldman , Thomas Walters , Samy Bengio , Douglas Eck , Jay M. Ponte , Ryan M. Rifkin
IPC分类号： G06F17/30
CPC分类号： G06F17/30772 , G06F17/30743
摘要： Generating a playlist may include designating a seed track in an audio library; identifying audio tracks in the audio library having constructs that are within a range of a corresponding construct of the seed track, where the constructs for the audio tracks are derived from frequency representations of the audio tracks, and the corresponding construct for the seed track is derived from a frequency representation of the seed track; and generating the playlist using at least some of the audio tracks that were identified.
摘要翻译：生成播放列表可以包括在音频库中指定种子轨道; 识别具有在种子轨道的相应构造的范围内的构造的音频库中的音轨，其中音频轨道的构造从音频轨道的频率表示导出，并且导出用于种子轨道的相应构造从种子轨迹的频率表示; 以及使用所识别的至少一些音轨生成播放列表。

10. 发明授权

US08515212B1 Image relevance model 有权
公开(公告)号：US08515212B1
公开(公告)日：2013-08-20
申请号：US12505287
申请日：2009-07-17
申请人： Samy Bengio , Erik Murphy-Chutorian , Yangli Hector Yee , Charles Rosenberg
发明人： Samy Bengio , Erik Murphy-Chutorian , Yangli Hector Yee , Charles Rosenberg
IPC分类号： G06F17/30268 , G06K9/66 , G06K9/6256 , G06F17/30265 , G06F17/30244 , G06F17/30247
CPC分类号： G06F17/30256 , G06F17/30271 , G06K9/6223 , G06K9/6262
摘要： Methods, systems, and apparatus, including computer program products, for identifying images relevant to a query are disclosed. An image search subsystem selects images to reference in image search results that are responsive to a query based on an image relevance model that is trained for the query. An independent image relevance model is trained for each unique query that is identified by the image search subsystem. The image relevance models can be applied to images to order image search results obtained for the query. Each relevance model is trained based on content feature values of images that are identified as being relevant to the query (e.g., frequently selected from the image search results) and images that are identified as being relevant to another unique query. The trained model is applied to the content feature values of all known images to generate an image relevance score that can be used to order search results for the query.

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