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1.发明授权公开(公告)号：US08660370B1
公开(公告)日：2014-02-25
申请号：US13755373
申请日：2013-01-31
Applicant: Google Inc.
Inventor： Sangho Yoon , Jay N. Yagnik , Mei Han , Vivek Kwatra
IPC: G06K9/62
CPC classification number: G06K9/6247 , G06K9/6223
Abstract: Clustering algorithms such as k-means clustering algorithm are used in applications that process entities with spatial and/or temporal characteristics, for example, media objects representing audio, video, or graphical data. Feature vectors representing characteristics of the entities are partitioned using clustering methods that produce results sensitive to an initial set of cluster seeds. The set of initial cluster seeds is generated using principal component analysis of either the complete feature vector set or a subset thereof. The feature vector set is divided into a desired number of initial clusters and a seed determined from each initial cluster.
Abstract translation: 诸如k均值聚类算法的聚类算法被用于处理具有空间和/或时间特征的实体的应用中，例如表示音频，视频或图形数据的媒体对象。 使用产生对初始集群种子集合敏感的结果的聚类方法对代表实体特征的特征向量进行分区。 使用完整特征向量集或其子集的主成分分析来生成初始簇种子集合。 特征向量集合被分为期望数量的初始簇和从每个初始簇确定的种子。
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公开(公告)号：US09679226B1
公开(公告)日：2017-06-13
申请号：US14671445
申请日：2015-03-27
Applicant: Google Inc.
Inventor： Qixing Huang , Mei Han , Bo Wu , Sergey Ioffe
CPC classification number: G06K9/627 , G06K9/00664 , G06K9/00684 , G06K9/6218 , G06K9/6223 , G06K9/6256 , G06K9/6272 , G06K9/628 , G06T7/11 , G06T2207/20016 , G06T2207/20072 , G06T2207/20081
Abstract: An image processing system automatically segments and labels an image using a hierarchical classification model. A global classification model determines initial labels for an image based on features of the image. A label-based descriptor is generated based on the initial labels. A local classification model is then selected from a plurality of learned local classification model based on the label-based descriptor. The local classification model is applied to the features of the input image to determined refined labels. The refined labels are stored in association with the input image.
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公开(公告)号：US08965124B1
公开(公告)日：2015-02-24
申请号：US13972806
申请日：2013-08-21
Applicant: Google Inc.
Inventor： Vivek Kwatra , Mei Han , Matthias Grundmann
CPC classification number: H04N5/91 , G06K9/00765
Abstract: A video is segmented to produce volumetric video regions. Descriptors are created for the video regions. A region graph is created for the video, where the region graph has weighted edges incident to video regions and the weight of an edge is calculated responsive to the descriptors of the video regions incident to the edge. The region graph is segmented responsive to the weights of the edges incident to the video regions to produce a new region graph having new volumetric video regions comprised of merged video regions of the first region graph. The descriptions of the region graphs are stored in a data storage.
Abstract translation: 视频被分割以产生体积视频区域。 为视频区域创建描述符。 为视频创建区域图，其中区域图具有入射到视频区域的加权边缘，并且响应于入射到边缘的视频区域的描述符来计算边缘的权重。 响应于入射到视频区域的边缘的权重来对区域图进行分段，以产生具有由第一区域图形的合并视频区域组成的新的体积视频区域的新区域图。 区域图的描述存储在数据存储器中。
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Patent Agency Ranking
公开(公告)号：US20140233811A1
公开(公告)日：2014-08-21
申请号：US13894341
申请日：2013-05-14
Applicant: Google Inc.
Inventor： Erik Murphy-Chutorian , Shengyang Dai , Ehud Rivlin , Chuck Joseph Rosenberg , Mei Han , Kyle Heath
IPC: G06K9/62
CPC classification number: G06K9/6267 , G06K9/00677 , G06K9/62
Abstract: Embodiments generally relate to summarizing a photo album. In one embodiment, a method includes grouping photos into a plurality of groups of photos, and selecting a plurality of representative photos, where each representative photo represents a respective group from the plurality of groups, where the selecting is based on a quality score of each of the photos, and where each quality score is based on different types of attributes. The method also includes providing the plurality of representative photos to a user.
Abstract translation: 实施例通常涉及总结相册。 在一个实施例中，一种方法包括将照片分组成多组照片，并且选择多个代表照片，其中每个代表照片表示来自多个组的相应组，其中选择基于每个组的质量得分 的照片，每个质量得分都基于不同类型的属性。 该方法还包括向用户提供多个有代表性的照片。
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公开(公告)号：US09311530B1
公开(公告)日：2016-04-12
申请号：US14520339
申请日：2014-10-21
Applicant: Google Inc.
Inventor： Erik Murphy-Chutorian , Charles Joseph Rosenberg , Shengyang Dai , Ehud Rivlin , Mei Han , Kyle Heath
CPC classification number: G06K9/00456 , G06K9/00221 , G06K9/00677 , G06K9/62 , G06K9/6267 , G06K2009/00328
Abstract: Embodiments generally relate to summarizing a photo album in a social network system. In one embodiment, a method includes grouping photos into a plurality of groups of photos, and selecting a plurality of representative photos, where each representative photo represents a respective group from the plurality of groups, where the selecting is based on a quality score of each of the photos, and where each quality score is based on different types of attributes. The method also includes enabling the plurality of representative photos to be shared.
Abstract translation: 实施例通常涉及在社交网络系统中总结相册。 在一个实施例中，一种方法包括将照片分组成多组照片，并且选择多个代表照片，其中每个代表照片表示来自多个组的相应组，其中选择基于每个组的质量得分 的照片，每个质量得分都是基于不同类型的属性。 该方法还包括使多个代表性照片能够共享。
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公开(公告)号：US09137529B1
公开(公告)日：2015-09-15
申请号：US14208352
申请日：2014-03-13
Applicant: Google Inc.
Inventor： Michele Covell , Mei Han , Saurabh Mathur , Shumeet Baluja , Vivek Kwatra
IPC: G06F15/18 , H04N19/105 , H04N19/17 , H04N19/189
CPC classification number: G06K9/6202 , G06F17/30247 , G06K9/00684 , G06K9/4604 , G06K9/6267 , G06K9/6292 , G06K9/66 , G06T1/0021 , H04N19/105 , H04N19/17 , H04N19/189
Abstract: An exemplar dictionary is built from exemplars of digital content for determining predictor blocks for encoding and decoding digital content. The exemplar dictionary organizes the exemplars as clusters of similar exemplars. Each cluster is mapped to a label. Machine learning techniques are used to generate a prediction model for predicting a label for an exemplar. The prediction model can be a hashing function that generates a hash key corresponding to the label for an exemplar. The prediction model learns from a training set based on the mapping from clusters to labels. A new mapping is obtained that improves a measure of association between clusters and labels. The new mapping is used to generate a new prediction model. This process is repeated in order to iteratively refine the machine learning modes generated.
Abstract translation: 由数字内容的示例构建示范字典，用于确定用于对数字内容进行编码和解码的预测器块。 示范字典将样本组织成类似样本的集群。 每个集群映射到一个标签。 机器学习技术用于生成用于预测样本的标签的预测模型。 预测模型可以是哈希函数，其生成与样本的标签相对应的散列密钥。 基于从集群到标签的映射，预测模型从训练集学习。 获得了一种新的映射，改进了集群和标签之间的关联度量。 新映射用于生成新的预测模型。 重复该过程以便迭代地改进所生成的机器学习模式。
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公开(公告)号：US08855437B1
公开(公告)日：2014-10-07
申请号：US13907000
申请日：2013-05-31
Applicant: Google, Inc.
Inventor： Jing-yu Cui , Vivek Kwatra , Michele Covell , Mei Han , Saurabh Mathur
CPC classification number: G06T9/004 , G06K9/6219
Abstract: Compression of an image is performed based on prediction of target blocks of an image from candidate source blocks of the image. Heuristics are used for identifying the candidate source blocks, for example, source blocks are selected from within a cluster of similar blocks obtained by K-means clustering. For each target block, a region adjacent to the target block is identified and a set of candidate source blocks along with candidate source regions adjacent to the candidate source blocks are identified. The candidate source regions are ranked based on the differences between the candidate source regions and the target source region. Each candidate source block is described using its rank and residual information describing differences between the candidate source block and the target block. The candidate source block that can be described using a minimum amount of information is selected for predicting the target block.
Abstract translation: 基于来自图像的候选源块的图像的目标块的预测来执行图像的压缩。 启发式用于识别候选源块，例如，从通过K均值聚类获得的相似块的簇内选择源块。 对于每个目标块，识别与目标块相邻的区域，并且识别一组候选源块以及与候选源块相邻的候选源区。 基于候选源区域和目标源区域之间的差异对候选源区域进行排序。 使用其等级和残差信息描述候选源块和目标块之间的差异来描述每个候选源块。 选择可以使用最小量的信息描述的候选源块用于预测目标块。
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8.发明授权公开(公告)号：US08787692B1
公开(公告)日：2014-07-22
申请号：US13946965
申请日：2013-07-19
Applicant: Google Inc.
Inventor： Mei Han , Michele Covell , Saurabh Mathur , Shumeet Baluja , Vivek Kwatra
IPC: G06K9/36
CPC classification number: G06K9/6219 , G06K9/6807 , H04N19/30 , H04N19/90
Abstract: An exemplar dictionary is built from example image blocks for determining predictor blocks for encoding and decoding images. The exemplar dictionary comprises a hierarchical organization of example image blocks. The hierarchical organization of image blocks is obtained by clustering a set of example image blocks, for example, based on k-means clustering. Performance of clustering is improved by transforming feature vectors representing the image blocks to fewer dimensions. Principal component analysis is used for determining feature vectors with fewer dimensions. The clustering performed at higher levels of the hierarchy uses fewer dimensions of feature vectors compared to lower levels of hierarchy. Performance of clustering is improved by processing only a sample of the image blocks of a cluster. The clustering performed at higher levels of the hierarchy uses lower sampling rates as compared to lower levels of hierarchy.
Abstract translation: 从用于确定用于对图像进行编码和解码的预测器块的示例图像块构建示范字典。 示例性字典包括示例图像块的分级组织。 通过例如基于k均值聚类来聚类一组示例图像块来获得图像块的分级组织。 通过将表示图像块的特征向量变换为较少的维度来提高聚类的性能。 主成分分析用于确定尺寸较小的特征向量。 在层次较高的层次上执行的聚类与较低级别的层次相比，使用较少的特征向量维度。 通过仅处理集群的图像块的样本来提高聚类的性能。 与较低级别的层次相比，在较高层次上执行的聚类使用较低的采样率。
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