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公开(公告)号：US09524449B2
公开(公告)日：2016-12-20
申请号：US14107191
申请日：2013-12-16
Applicant: Adobe Systems Incorporated
Inventor： Jianchao Yang , Guang Chen , Hailin Jin , Jonathan Brandt , Elya Shechtman , Aseem Omprakash Agarwala
CPC classification number: G06K9/6267 , G06K9/6219 , G06K9/6282 , G06K9/6807
Abstract: Example systems and methods for classifying visual patterns into a plurality of classes are presented. Using reference visual patterns of known classification, at least one image or visual pattern classifier is generated, which is then employed to classify a plurality of candidate visual patterns of unknown classification. The classification scheme employed may be hierarchical or nonhierarchical. The types of visual patterns may be fonts, human faces, or any other type of visual patterns or images subject to classification.
Abstract translation: 提出了将视觉模式分类为多个类的示例系统和方法。 使用已知分类的参考视觉图案，生成至少一个图像或视觉模式分类器，然后将其用于对未知分类的多个候选视觉图案进行分类。 所使用的分类方案可以是分层的或非分层的。 视觉图案的类型可以是字体，人脸或任何其他类型的可分类的视觉图案或图像。
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公开(公告)号：US09436893B2
公开(公告)日：2016-09-06
申请号：US14091972
申请日：2013-11-27
Applicant: Adobe Systems Incorporated
Inventor： Jianchao Yang , Zhaowen Wang , Zhe Lin , Jonathan Brandt
IPC: G06K9/62
CPC classification number: G06K9/6269 , G06K9/6235
Abstract: A system and method for distributed similarity learning for high-dimensional image features are described. A set of data features is accessed. Subspaces from a space formed by the set of data features are determined using a set of projection matrices. Each subspace has a dimension lower than a dimension of the set of data features. Similarity functions are computed for the subspaces. Each similarity function is based on the dimension of the corresponding subspace. A linear combination of the similarity functions is performed to determine a similarity function for the set of data features.
Abstract translation: 描述了用于高维图像特征的分布式相似性学习的系统和方法。 访问一组数据功能。 使用一组投影矩阵来确定由该组数据特征形成的空间的子空间。 每个子空间的尺寸小于数据特征集合的维度。 为子空间计算相似度函数。 每个相似度函数基于相应子空间的维度。 执行相似度函数的线性组合以确定该组数据特征的相似度函数。
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公开(公告)号：US09317534B2
公开(公告)日：2016-04-19
申请号：US13919312
申请日：2013-06-17
Applicant: Adobe Systems Incorporated
Inventor： Jonathan Brandt
IPC: G06F17/30
CPC classification number: G06F17/30277 , G06F17/30274 , G06F17/30991
Abstract: An image search method includes receiving a first query, the first query providing a first image constraint. A first search of a plurality of images is performed, responsive to the first query, to identify a first set of images satisfying the first constraint. A first search result, which includes the first set of images identified as satisfying the first constraint, is presented. A second query is received, the second query providing a second image constraint with reference to a first image of the first set of images. A second search of the plurality of images is performed, responsive to the second query, to identify a second set of images that satisfy the second constraint. A second search result, which includes the second set of images identified as satisfying the second constraint, is presented.
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Patent Agency Ranking
公开(公告)号：US09224066B2
公开(公告)日：2015-12-29
申请号：US13782735
申请日：2013-03-01
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Jonathan Brandt , Xiaohui Shen
CPC classification number: G06K9/6202 , G06K9/00221 , G06K9/00228 , G06K9/3241 , G06K9/685
Abstract: One exemplary embodiment involves receiving, at a computing device comprising a processor, a test image having a candidate object and a set of object images detected to depict a similar object as the test image. The embodiment involves localizing the object depicted in each one of the object images based on the candidate object in the test image to determine a location of the object in each respective object image and then generating a validation score for the candidate object in the test image based at least in part on the determined location of the object in the respective object image and known location of the object in the same respective object image. The embodiment also involves computing a final detection score for the candidate object based on the validation score that indicates a confidence level that the object in the test image is located as indicated by the candidate object.
Abstract translation: 一个示例性实施例涉及在包括处理器的计算设备处接收具有候选对象的测试图像和检测到的用于描绘与测试图像相似的对象的一组对象图像。 该实施例涉及基于测试图像中的候选对象来定位每个对象图像中描绘的对象，以确定对象在每个相应对象图像中的位置，然后在基于测试图像的基础上生成候选对象的验证分数 至少部分地基于相应对象图像中的对象的确定位置和相同对象图像中的对象的已知位置。 该实施例还涉及基于指示由候选对象指示的测试图像中的对象所位于的置信水平的验证分数来计算候选对象的最终检测分数。
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公开(公告)号：US09129152B2
公开(公告)日：2015-09-08
申请号：US14080010
申请日：2013-11-14
Applicant: Adobe Systems Incorporated
Inventor： Jonathan Brandt , Zhe Lin , Brandon M. Smith
CPC classification number: G06K9/00295 , G06K9/00 , G06K9/00268 , G06K9/00275 , G06K9/4633 , G06K9/6215
Abstract: In an example embodiment, for each of the image exemplars, a first location offset between an actual landmark location for a first landmark in the image exemplar and a predicted landmark location for the first landmark in the image exemplar is determined. Then, a probability that the image recognition process applied using the first feature produces an accurate identification of the first landmark in the image exemplars is determined based on the first location offsets for each of the image exemplars. A weight may then be assigned to the first feature based on the derived probability. An image recognition process may then be performed on an image, the image recognition process utilizing a voting process, for each of one or more features, for one or more landmarks in the plurality of image exemplars, the voting process for the first feature weighted according to the weight assigned to the first feature.
Abstract translation: 在示例实施例中，对于每个图像样本，确定在图像样本中的第一地标的实际地标位置与图像样本中的第一地标的预测地标位置之间的第一位置偏移。 然后，基于每个图像样本的第一位置偏移来确定使用第一特征应用的图像识别处理产生图像样本中的第一地标的精确识别的概率。 然后可以基于导出的概率将权重分配给第一特征。 然后可以对图像执行图像识别处理，对于多个图像样本中的一个或多个地标，针对一个或多个特征中的每一个利用投票处理的图像识别处理，对第一特征的投票处理根据 分配给第一个特征的权重。
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公开(公告)号：US09122960B2
公开(公告)日：2015-09-01
申请号：US13691212
申请日：2012-11-30
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Xin Lu , Jonathan Brandt , Hailin Jin
CPC classification number: G06K9/68 , G06T5/001 , G06T2207/20021
Abstract: Systems and methods are provided for providing patch size adaptation for patch-based image enhancement operations. In one embodiment, an image manipulation application receives an input image. The image manipulation application compares a value for an attribute of at least one input patch of the input image to a threshold value. Based on comparing the value for the to the threshold value, the image manipulation application adjusts a first patch size of the input patch to a second patch size that improves performance of a patch-based image enhancement operation as compared to the first patch size. The image manipulation application performs the patch-based image enhancement operation based on one or more input patches of the input image having the second patch size.
Abstract translation: 提供了系统和方法，用于为基于贴片的图像增强操作提供补丁大小适应。 在一个实施例中，图像处理应用接收输入图像。 图像处理应用将输入图像的至少一个输入片段的属性的值与阈值进行比较。 基于比较阈值的值，图像处理应用程序将输入补丁的第一补丁大小调整为与第一补丁大小相比提高基于补丁的图像增强操作的性能的第二补丁大小。 图像处理应用程序基于具有第二补丁大小的输入图像的一个或多个输入补丁执行基于补丁的图像增强操作。
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公开(公告)号：US20150170000A1
公开(公告)日：2015-06-18
申请号：US14107191
申请日：2013-12-16
Applicant: Adobe Systems Incorporated
Inventor： Jianchao Yang , Guang Chen , Hailin Jin , Jonathan Brandt , Elya Shechtman , Aseem Omprakash Agarwala
IPC: G06K9/62
CPC classification number: G06K9/6267 , G06K9/6219 , G06K9/6282 , G06K9/6807
Abstract: Example systems and methods for classifying visual patterns into a plurality of classes are presented. Using reference visual patterns of known classification, at least one image or visual pattern classifier is generated, which is then employed to classify a plurality of candidate visual patterns of unknown classification. The classification scheme employed may be hierarchical or nonhierarchical. The types of visual patterns may be fonts, human faces, or any other type of visual patterns or images subject to classification.
Abstract translation: 提出了将视觉模式分类为多个类的示例系统和方法。 使用已知分类的参考视觉图案，生成至少一个图像或视觉模式分类器，然后将其用于对未知分类的多个候选视觉图案进行分类。 所使用的分类方案可以是分层的或非分层的。 视觉图案的类型可以是字体，人脸或任何其他类型的可分类的视觉图案或图像。
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公开(公告)号：US20150063713A1
公开(公告)日：2015-03-05
申请号：US14012770
申请日：2013-08-28
Applicant: Adobe Systems Incorporated
Inventor： Jianchao Yang , Guang Chen , Hailin Jin , Jonathan Brandt , Elya Shechtman
IPC: G06K9/62
CPC classification number: G06K9/6267 , G06K9/00979 , G06K9/4671 , G06K9/6219 , G06K9/6298 , G06K9/6828
Abstract: A hierarchy machine may be configured as a clustering machine that utilizes local feature embedding to organize visual patterns into nodes that each represent one or more visual patterns. These nodes may be arranged as a hierarchy in which a node may have a parent-child relationship with one or more other nodes. The hierarchy machine may implement a node splitting and tree-learning algorithm that includes hard-splitting of nodes and soft-assignment of nodes to perform error-bounded splitting of nodes into clusters. This may enable the hierarchy machine, which may form all or part of a visual pattern recognition system, to perform large-scale visual pattern recognition, such as font recognition or facial recognition, based on a learned error-bounded tree of visual patterns.
Abstract translation: 层次机器可以被配置为利用局部特征嵌入将可视图案组织成每个表示一个或多个视觉图案的节点的聚类机器。 这些节点可以被布置为其中节点可以与一个或多个其他节点具有父子关系的层级。 层次机器可以实现节点分割和树学习算法，其包括节点的硬分割和节点的软分配，以执行节点到分簇的有界限制的分割。 这可以使得可以形成视觉图案识别系统的全部或一部分的层次机器基于学习的有界错误的视觉图案树来执行诸如字体识别或面部识别的大规模视觉模式识别。
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