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    • 1. 发明授权
    • Training of adapted classifiers for video categorization
    • 适应分类器的视频分类培训
    • US08452778B1
    • 2013-05-28
    • US12874015
    • 2010-09-01
    • Yang SongMing ZhaoJay Yagnik
    • Yang SongMing ZhaoJay Yagnik
    • G06F17/30G10L19/12
    • G06F17/30799G06F17/30796G06K9/00711
    • 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.
    • 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。
    • 2. 发明授权
    • Method and system for automated annotation of persons in video content
    • 视频内容中用户自动注释的方法和系统
    • US08213689B2
    • 2012-07-03
    • US12172939
    • 2008-07-14
    • Jay YagnikMing Zhao
    • Jay YagnikMing Zhao
    • G06K9/00G06K9/62
    • G06K9/00711G06F17/30781G06K9/00295G06K9/6255
    • Methods and systems for automated annotation of persons in video content are disclosed. In one embodiment, a method of identifying faces in a video includes the stages of: generating face tracks from input video streams; selecting key face images for each face track; clustering the face tracks to generate face clusters; creating face models from the face clusters; and correlating face models with a face model database. In another embodiment, a system for identifying faces in a video includes a face model database having face entries with face models and corresponding names, and a video face identifier module. In yet another embodiment, the system for identifying faces in a video can also have a face model generator.
    • 公开了用于视频内容中的人的自动注释的方法和系统。 在一个实施例中,识别视频中的面部的方法包括以下阶段:从输入视频流生成面部曲面; 为每个脸部轨迹选择关键脸部图像; 聚集脸部轨迹以生成脸部群集; 从脸部群集中创建面部模型; 并将面部模型与面部模型数据库相关联。 在另一个实施例中,用于识别视频中的面部的系统包括具有面部表情和面部模型和对应名称的面部模型数据库,以及视频面部识别器模块。 在另一个实施例中,用于识别视频中的面部的系统还可以具有面部模型生成器。
    • 4. 发明授权
    • Automatic large scale video object recognition
    • 自动大规模视频对象识别
    • US08792732B1
    • 2014-07-29
    • US13559420
    • 2012-07-26
    • Ming ZhaoJay Yagnik
    • Ming ZhaoJay Yagnik
    • G06K9/46H04N5/14
    • G06K9/6232G06K9/6215
    • An object recognition system performs a number of rounds of dimensionality reduction and consistency learning on visual content items such as videos and still images, resulting in a set of feature vectors that accurately predict the presence of a visual object represented by a given object name within an visual content item. The feature vectors are stored in association with the object name which they represent and with an indication of the number of rounds of dimensionality reduction and consistency learning that produced them. The feature vectors and the indication can be used for various purposes, such as quickly determining a visual content item containing a visual representation of a given object name.
    • 对象识别系统对诸如视频和静止图像的视觉内容项目执行多次维数降低和一致性学习,导致一组特征向量,其精确地预测由一个对象名称表示的视觉对象的存在 视觉内容项目。 特征向量与它们所代表的对象名称相关联地存储,并且显示产生它们的维度降低和一致性学习的轮次数。 特征向量和指示可以用于各种目的,诸如快速确定包含给定对象名称的视觉表示的视觉内容项。
    • 7. 发明授权
    • Automatic large scale video object recognition
    • 自动大规模视频对象识别
    • US08254699B1
    • 2012-08-28
    • US12364390
    • 2009-02-02
    • Ming ZhaoJay Yagnik
    • Ming ZhaoJay Yagnik
    • G06K9/46G06K9/48H04N5/14G06E1/00
    • G06K9/6232G06K9/6215
    • An object recognition system performs a number of rounds of dimensionality reduction and consistency learning on visual content items such as videos and still images, resulting in a set of feature vectors that accurately predict the presence of a visual object represented by a given object name within an visual content item. The feature vectors are stored in association with the object name which they represent and with an indication of the number of rounds of dimensionality reduction and consistency learning that produced them. The feature vectors and the indication can be used for various purposes, such as quickly determining a visual content item containing a visual representation of a given object name.
    • 对象识别系统对诸如视频和静止图像的视觉内容项目执行多次维数降低和一致性学习,导致一组特征向量,其精确地预测由一个对象名称表示的视觉对象的存在 视觉内容项目。 特征向量与它们所代表的对象名称相关联地存储,并且显示产生它们的维度降低和一致性学习的轮次数。 特征向量和指示可以用于各种目的,诸如快速确定包含给定对象名称的视觉表示的视觉内容项。
    • 9. 发明授权
    • Matching based upon rank
    • 基于等级匹配
    • US08805090B1
    • 2014-08-12
    • US13368317
    • 2012-02-07
    • Jay YagnikSergey Ioffe
    • Jay YagnikSergey Ioffe
    • G06K9/68
    • G06K9/6212
    • Systems and methods for measuring consistency between two objects based upon a rank of object elements instead of based upon the values of those object elements. Objects being compared can be represented by d-dimension feature vectors, U and V, where each dimension includes an associated value. U and V can be converted to rank vectors, P and Q, where values of U and V dimensions are replaced by an ordered rank or a function thereof. Analysis directed to the consistency between U and V can be accomplished by determining consistency between P and Q, which can be more efficient and more accurate, particularly with regard to illumination-invariant comparisons.
    • 基于对象元素的等级而不是基于这些对象元素的值来测量两个对象之间的一致性的系统和方法。 被比较的对象可以由d维特征向量U和V表示,其中每个维度包括相关联的值。 U和V可以被转换为等级向量P和Q,其中U和V维度的值被有序等级或其功能所代替。 可以通过确定P和Q之间的一致性来实现对U和V之间的一致性的分析,这可以更有效和更准确,特别是在照明不变比较方面。
    • 10. 发明授权
    • Detection and classification of matches between time-based media
    • 基于时间的媒体之间的匹配检测和分类
    • US08238669B2
    • 2012-08-07
    • US12174366
    • 2008-07-16
    • Michele CovellJay YagnikJeff FaustShumeet Baluja
    • Michele CovellJay YagnikJeff FaustShumeet Baluja
    • G06K9/62H04N7/10H04N7/025
    • G06K9/00758G06F17/30784
    • A system and method detects matches between portions of video content. A matching module receives an input video fingerprint representing an input video and a set of reference fingerprints representing reference videos in a reference database. The matching module compares the reference fingerprints and input fingerprints to generate a list of candidate segments from the reference video set. Each candidate segment comprises a time-localized portion of a reference video that potentially matches the input video. A classifier is applied to each of the candidate segments to classify the segment as a matching segment or a non-matching segment. A result is then outputted identifying a matching portion of a reference video from the reference video set based on the segments classified as matches.
    • 系统和方法检测视频内容的部分之间的匹配。 匹配模块接收表示参考数据库中的参考视频的输入视频和一组参考指纹的输入视频指纹。 匹配模块比较参考指纹和输入指纹,以从参考视频集中生成候选片段的列表。 每个候选片段包括潜在地匹配输入视频的参考视频的时间局部化部分。 将分类器应用于每个候选片段以将片段分类为匹配片段或非匹配片段。 然后基于被分类为匹配的段,从参考视频集中输出标识参考视频的匹配部分的结果。