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1. 发明申请

US20120251007A1 Robust Large-Scale Visual Codebook Construction 有权
标题翻译：坚固的大型视觉代码簿构建
公开(公告)号：US20120251007A1
公开(公告)日：2012-10-04
申请号：US13077735
申请日：2011-03-31
申请人： Linjun Yang , Darui Li , Xian-Sheng Hua , Hong-Jiang Zhang
发明人： Linjun Yang , Darui Li , Xian-Sheng Hua , Hong-Jiang Zhang
IPC分类号： G06K9/46
CPC分类号： G06K9/6223
摘要： Techniques for construction of a visual codebook are described herein. Feature points may be extracted from large numbers of images. In one example, images providing N feature points may be used to construct a codebook of K words. The centers of each of K clusters of feature points may be initialized. In a looping or iterative manner, an assignment step assigns each feature point to a cluster and an update step locates a center of each cluster. The feature points may be assigned to a cluster based on a lesser of a distance to a center of a previously assigned cluster and a distance to a center derived by operation of an approximate nearest neighbor algorithm having aspects of randomization. The loop terminates when the feature points have sufficiently converged to their respective clusters. Centers of the clusters represent visual words, which may be used to construct the visual codebook.
摘要翻译：本文描述了构建视觉码本的技术。特征点可以从大量图像中提取出来。在一个示例中，提供N个特征点的图像可以用于构造K个字的码本。可以初始化K个特征点中的每一个的中心。以循环或迭代的方式，分配步骤将每个特征点分配给集群，并且更新步骤定位每个集群的中心。可以基于距先前分配的簇的中心的距离中较小的一个特征点来分配特征点，以及通过具有随机化方面的近似最近邻算法的操作导出的到中心的距离。当特征点已经充分收敛到它们各自的簇时，环路终止。集群的中心表示视觉词，可用于构建视觉码本。

2. 发明授权

US08422802B2 Robust large-scale visual codebook construction 有权
标题翻译：坚固的大型视觉代码簿建设
公开(公告)号：US08422802B2
公开(公告)日：2013-04-16
申请号：US13077735
申请日：2011-03-31
申请人： Linjun Yang , Darui Li , Xian-Sheng Hua , Hong-Jiang Zhang
发明人： Linjun Yang , Darui Li , Xian-Sheng Hua , Hong-Jiang Zhang
IPC分类号： G06K9/36
CPC分类号： G06K9/6223
摘要： Techniques for construction of a visual codebook are described herein. Feature points may be extracted from large numbers of images. In one example, images providing N feature points may be used to construct a codebook of K words. The centers of each of K clusters of feature points may be initialized. In a looping or iterative manner, an assignment step assigns each feature point to a cluster and an update step locates a center of each cluster. The feature points may be assigned to a cluster based on a lesser of a distance to a center of a previously assigned cluster and a distance to a center derived by operation of an approximate nearest neighbor algorithm having aspects of randomization. The loop terminates when the feature points have sufficiently converged to their respective clusters. Centers of the clusters represent visual words, which may be used to construct the visual codebook.
摘要翻译：本文描述了构建视觉码本的技术。特征点可以从大量图像中提取出来。在一个示例中，提供N个特征点的图像可以用于构造K个字的码本。可以初始化K个特征点中的每一个的中心。以循环或迭代的方式，分配步骤将每个特征点分配给集群，并且更新步骤定位每个集群的中心。可以基于距先前分配的簇的中心的距离中较小的一个特征点来分配特征点，以及通过具有随机化方面的近似最近邻算法的操作导出的到中心的距离。当特征点已经充分收敛到它们各自的簇时，环路终止。集群的中心表示视觉词，可用于构建视觉码本。

3. 发明授权

US08175847B2 Tag ranking 有权
标题翻译：标签排名
公开(公告)号：US08175847B2
公开(公告)日：2012-05-08
申请号：US12415722
申请日：2009-03-31
申请人： Hong-Jiang Zhang , Dong Liu , Meng Wang , Linjun Yang , Xian-Sheng Hua
发明人： Hong-Jiang Zhang , Dong Liu , Meng Wang , Linjun Yang , Xian-Sheng Hua
IPC分类号： G06F17/18
CPC分类号： G06F17/30038
摘要： Technologies for generating a boosted tag ranking for a media instance, the boosted tag ranking based on probabilistic relevance estimation computed by a probabilistic relevance estimator and tag correlation refining performed by a tag correlation refiner. Such boosted tag rankings may be used for search result ranking, tag recommendation, and group recommendation.
摘要翻译：用于生成用于媒体实例的提升的标签排名的技术，基于由概率相关性估计器计算的概率相关性估计的增强的标签排名以及由标签相关性精炼器执行的标签相关性精炼。这种提升的标签排名可以用于搜索结果排名，标签推荐和组推荐。

4. 发明授权

US09047319B2 Tag association with image regions 有权
标题翻译：标签与图像区域的关联
公开(公告)号：US09047319B2
公开(公告)日：2015-06-02
申请号：US12972203
申请日：2010-12-17
申请人： Xian-Sheng Hua , Kuiyuan Yang , Meng Wang , Hong-Jiang Zhang
发明人： Xian-Sheng Hua , Kuiyuan Yang , Meng Wang , Hong-Jiang Zhang
IPC分类号： G06F17/30 , G06K9/00
CPC分类号： G06F17/30256 , G06F17/30268
摘要： A computing device configured to determine that one or more regions of an image are associated with a tag of the image is described herein. The computing device is further configured to determine one or more attribute tags describing at least one of the content or context of the one or more regions. Upon determining the attribute tags, the computing device associates the attribute tags with the tag to enable image searching based on the tag and attribute tags.
摘要翻译：在此描述了被配置为确定图像的一个或多个区域与图像的标签相关联的计算设备。计算设备还被配置为确定描述一个或多个区域的内容或上下文中的至少一个的一个或多个属性标签。在确定属性标签之后，计算设备将属性标签与标签相关联，以便基于标签和属性标签启用图像搜索。

5. 发明申请

US20120158721A1 Tag Association with Image Regions 有权
标题翻译：标签与图像区域的关联
公开(公告)号：US20120158721A1
公开(公告)日：2012-06-21
申请号：US12972203
申请日：2010-12-17
申请人： Xian-Sheng Hua , Kuiyuan Yang , Meng Wang , Hong-Jiang Zhang
发明人： Xian-Sheng Hua , Kuiyuan Yang , Meng Wang , Hong-Jiang Zhang
IPC分类号： G06F17/30
CPC分类号： G06F17/30256 , G06F17/30268
摘要： A computing device configured to determine that one or more regions of an image are associated with a tag of the image is described herein. The computing device is further configured to determine one or more attribute tags describing at least one of the content or context of the one or more regions. Upon determining the attribute tags, the computing device associates the attribute tags with the tag to enable image searching based on the tag and attribute tags.
摘要翻译：在此描述了被配置为确定图像的一个或多个区域与图像的标签相关联的计算设备。计算设备还被配置为确定描述一个或多个区域的内容或上下文中的至少一个的一个或多个属性标签。在确定属性标签之后，计算设备将属性标签与标签相关联，以便基于标签和属性标签启用图像搜索。

6. 发明申请

US20090290802A1 CONCURRENT MULTIPLE-INSTANCE LEARNING FOR IMAGE CATEGORIZATION 审中-公开
标题翻译：一致的多元学习图像分类
公开(公告)号：US20090290802A1
公开(公告)日：2009-11-26
申请号：US12125057
申请日：2008-05-22
申请人： Xian-Sheng Hua , Guo-Jun Qi , Yong Rui , Tao Mei , Hong-Jiang Zhang
发明人： Xian-Sheng Hua , Guo-Jun Qi , Yong Rui , Tao Mei , Hong-Jiang Zhang
IPC分类号： G06K9/62
CPC分类号： G06K9/34
摘要： The concurrent multiple instance learning technique described encodes the inter-dependency between instances (e.g. regions in an image) in order to predict a label for a future instance, and, if desired the label for an image determined from the label of these instances. The technique, in one embodiment, uses a concurrent tensor to model the semantic linkage between instances in a set of images. Based on the concurrent tensor, rank-1 supersymmetric non-negative tensor factorization (SNTF) can be applied to estimate the probability of each instance being relevant to a target category. In one embodiment, the technique formulates the label prediction processes in a regularization framework, which avoids overfitting, and significantly improves a learning machine's generalization capability, similar to that in SVMs. The technique, in one embodiment, uses Reproducing Kernel Hilbert Space (RKHS) to extend predicted labels to the whole feature space based on the generalized representer theorem.
摘要翻译：所描述的并发多实例学习技术编码实例（例如，图像中的区域）之间的相互依赖性，以便预测将来实例的标签，以及如果需要，从这些实例的标签确定的图像的标签。在一个实施例中，该技术使用并发张量来对一组图像中的实例之间的语义联系进行建模。基于并发张量，可以应用秩1超对称非负张量因子分解（SNTF）来估计每个实例与目标类别相关的概率。在一个实施例中，该技术在正则化框架中制定标签预测过程，其避免过拟合，并且显着地提高学习机器的泛化能力，类似于SVM中的标准预测过程。在一个实施例中，该技术使用再生核希尔伯特空间（RKHS）来基于广义代表定理将预测标签扩展到整个特征空间。

7. 发明申请

US20090125461A1 Multi-Label Active Learning 有权
标题翻译：多标签主动学习
公开(公告)号：US20090125461A1
公开(公告)日：2009-05-14
申请号：US11958050
申请日：2007-12-17
申请人： Guo-Jun Qi , Xian-Sheng Hua , Yong Rui , Hong-Jiang Zhang , Shipeng Li
发明人： Guo-Jun Qi , Xian-Sheng Hua , Yong Rui , Hong-Jiang Zhang , Shipeng Li
IPC分类号： G06F15/18
CPC分类号： G06N99/005
摘要： Multi-label active learning may entail training a classifier with a set of training samples having multiple labels per sample. In an example embodiment, a method includes accepting a set of training samples, with the set of training samples having multiple respective samples that are each respectively associated with multiple labels. The set of training samples is analyzed to select a sample-label pair responsive to at least one error parameter. The selected sample-label pair is then submitted to an oracle for labeling.
摘要翻译：多标签主动学习可能需要对分类器训练一组具有每个样本的多个标签的训练样本。在示例实施例中，一种方法包括接受一组训练样本，其中该组训练样本具有多个相应样本，每个样本分别与多个标签相关联。分析该组训练样本以响应于至少一个误差参数来选择样本标签对。然后将选定的样品标签对提交给oracle进行标记。

8. 发明授权

US07164798B2 Learning-based automatic commercial content detection 失效
标题翻译：基于学习的自动商业内容检测
公开(公告)号：US07164798B2
公开(公告)日：2007-01-16
申请号：US10368235
申请日：2003-02-18
申请人： Xian-Sheng Hua , Lie Lu , Mingjing Li , Hong-Jiang Zhang
发明人： Xian-Sheng Hua , Lie Lu , Mingjing Li , Hong-Jiang Zhang
IPC分类号： G06K9/72 , H04H9/00
CPC分类号： G06K9/00711
摘要： Systems and methods for learning-based automatic commercial content detection are described. In one aspect, program data is divided into multiple segments. The segments are analyzed to determine visual, audio, and context-based feature sets that differentiate commercial content from non-commercial content. The context-based features are a function of single-side left and/or right neighborhoods of segments of the multiple segments.
摘要翻译：描述了基于学习的自动商业内容检测的系统和方法。在一个方面，程序数据被分成多个段。分析段以确定将商业内容与非商业内容区分开的视觉，音频和基于上下文的特征集。基于上下文的特征是多个段的单侧左和/或右邻域的函数。

9. 发明授权

US07565016B2 Learning-based automatic commercial content detection 失效
标题翻译：基于学习的自动商业内容检测
公开(公告)号：US07565016B2
公开(公告)日：2009-07-21
申请号：US11623304
申请日：2007-01-15
申请人： Xian-Sheng Hua , Lie Lu , Mingjing Li , Hong-Jiang Zhang
发明人： Xian-Sheng Hua , Lie Lu , Mingjing Li , Hong-Jiang Zhang
IPC分类号： G06K9/72 , H04H9/00
CPC分类号： G06K9/00711
摘要： Systems and methods for learning-based automatic commercial content detection are described. In one aspect, the systems and methods include a training component and an analyzing component. The training component trains a commercial content classification model using a kernel support vector machine. The analyzing component analyzes program data such as video and audio data using the commercial content classification model and one or more of single-side left neighborhood(s) and right neighborhood(s) of program data segments. Based on this analysis, each of the program data segments are classified as being commercial or non-commercial segments.
摘要翻译：描述了基于学习的自动商业内容检测的系统和方法。一方面，系统和方法包括训练组件和分析组件。训练组件使用内核支持向量机训练商业内容分类模型。分析组件使用商业内容分类模型和程序数据段的单侧左邻居和右邻居中的一个或多个来分析诸如视频和音频数据的程序数据。基于此分析，每个程序数据段被分类为商业或非商业领域。

10. 发明申请

US20120158686A1 Image Tag Refinement 审中-公开
标题翻译：图像标签细化
公开(公告)号：US20120158686A1
公开(公告)日：2012-06-21
申请号：US12971880
申请日：2010-12-17
申请人： Xian-Sheng Hua , Dong Liu , Meng Wang , Hong-Jiang Zhang
发明人： Xian-Sheng Hua , Dong Liu , Meng Wang , Hong-Jiang Zhang
IPC分类号： G06F17/30
CPC分类号： G06F16/5866
摘要： A computing device configured to determine a subset of the tags associated with at least one image of a plurality of received, tagged images is described herein. The computing device performs the determining based on one or more measures of consistency of visual similarity between ones of the images with semantic similarity between tags of the ones of the images.
摘要翻译：被配置为确定与多个接收到的标记图像中的至少一个图像相关联的标签的子集的计算设备在此被描述。计算装置基于图像中的一个图像之间的视觉相似性的一致性的测量来执行确定，其中图像的标签之间具有语义相似性。

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