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公开(公告)号：US20170236032A1
公开(公告)日：2017-08-17
申请号：US15043174
申请日：2016-02-12
Applicant: ADOBE SYSTEMS INCORPORATED
Inventor： ZHE LIN , XIAOHUI SHEN , JONATHAN BRANDT , JIANMING ZHANG , CHEN FANG
CPC classification number: G06K9/623 , G06F16/24578 , G06F16/285 , G06F16/51 , G06F16/583 , G06K9/4628 , G06K9/6223 , G06K9/6262 , G06K9/6276 , G06N3/0454 , G06N3/08 , G06N20/10
Abstract: Embodiments of the present invention provide an automated image tagging system that can predict a set of tags, along with relevance scores, that can be used for keyword-based image retrieval, image tag proposal, and image tag auto-completion based on user input. Initially, during training, a clustering technique is utilized to reduce cluster imbalance in the data that is input into a convolutional neural network (CNN) for training feature data. In embodiments, the clustering technique can also be utilized to compute data point similarity that can be utilized for tag propagation (to tag untagged images). During testing, a diversity based voting framework is utilized to overcome user tagging biases. In some embodiments, bigram re-weighting can down-weight a keyword that is likely to be part of a bigram based on a predicted tag set.
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公开(公告)号：US20170061257A1
公开(公告)日：2017-03-02
申请号：US15349876
申请日：2016-11-11
Applicant: ADOBE SYSTEMS INCORPORATED
Inventor： JIANCHAO YANG , GUANG CHEN , HAILIN JIN , JONATHAN BRANDT , ELYA SHECHTMAN , ASEEM OMPRAKASH AGARWALA
CPC classification number: G06K9/6282 , G06K9/6219 , G06K9/6267 , 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：US20170060580A1
公开(公告)日：2017-03-02
申请号：US14834730
申请日：2015-08-25
Applicant: ADOBE SYSTEMS INCORPORATED
Inventor： JONATHAN BRANDT
IPC: G06F9/30
CPC classification number: G06Q10/06
Abstract: In various implementations, an abstraction is generated from an asset associated with an asset-modifying workflow. The abstraction can be embedded into an activity stream generated from an asset-modification application and communicated to a remote server device for collection and analysis. The remote server device, upon receiving at least the abstraction, can determine a contextual identifier for association with the abstraction and the asset associated with the asset-modifying workflow. The remote server device can conduct usage analysis on data received from the activity stream in association with the contextual identifier, and further send a signal to the asset-modification application to customize the workflow based on the contextual identifier determined to be associated with the abstraction and asset.
Abstract translation: 在各种实现中，从与资产修改工作流相关联的资产生成抽象。 抽象可以嵌入到从资产修改应用程序生成的活动流中，并传达到远程服务器设备进行收集和分析。 远程服务器设备至少在接收到抽象时可以确定与抽象和与资产修改工作流相关联的资产关联的上下文标识符。 远程服务器设备可以与上下文标识符相关联地对从活动流接收的数据进行使用分析，并且进一步向资产修改应用发送信号，以基于被确定为与抽象相关联的上下文标识来定制工作流，并且 资产
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Patent Agency Ranking
公开(公告)号：US20190147224A1
公开(公告)日：2019-05-16
申请号：US15815635
申请日：2017-11-16
Applicant: ADOBE SYSTEMS INCORPORATED
Inventor： HAOXIANG LI , ZHE LIN , JONATHAN BRANDT , XIAOHUI SHEN
Abstract: Approaches are described for determining facial landmarks in images. An input image is provided to at least one trained neural network that determines a face region (e.g., bounding box of a face) of the input image and initial facial landmark locations corresponding to the face region. The initial facial landmark locations are provided to a 3D face mapper that maps the initial facial landmark locations to a 3D face model. A set of facial landmark locations are determined from the 3D face model. The set of facial landmark locations are provided to a landmark location adjuster that adjusts positions of the set of facial landmark locations based on the input image. The input image is presented on a user device using the adjusted set of facial landmark locations.
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5.发明申请公开(公告)号：US20160364633A1
公开(公告)日：2016-12-15
申请号：US14734466
申请日：2015-06-09
Applicant: ADOBE SYSTEMS INCORPORATED
Inventor： JIANCHAO YANG , ZHANGYANG WANG , JONATHAN BRANDT , HAILIN JIN , ELYA SHECHTMAN , ASEEM OMPRAKASH AGARWALA
CPC classification number: G06T3/40 , G06K9/6255 , G06K9/6828
Abstract: A convolutional neural network (CNN) is trained for font recognition and font similarity learning. In a training phase, text images with font labels are synthesized by introducing variances to minimize the gap between the training images and real-world text images. Training images are generated and input into the CNN. The output is fed into an N-way softmax function dependent on the number of fonts the CNN is being trained on, producing a distribution of classified text images over N class labels. In a testing phase, each test image is normalized in height and squeezed in aspect ratio resulting in a plurality of test patches. The CNN averages the probabilities of each test patch belonging to a set of fonts to obtain a classification. Feature representations may be extracted and utilized to define font similarity between fonts, which may be utilized in font suggestion, font browsing, or font recognition applications.
Abstract translation: 对卷积神经网络（CNN）进行字体识别和字体相似学习。 在训练阶段，通过引入差异来合成具有字体标签的文本图像，以最小化训练图像与真实世界文本图像之间的差距。 生成训练图像并将其输入到CNN中。 根据CNN正在训练的字体数量，输出被输入到N-way softmax函数中，产生N类标签上分类文本图像的分布。 在测试阶段，每个测试图像的高度被标准化，并以纵横比挤压，从而产生多个测试贴片。 CNN对属于一组字体的每个测试补丁的概率进行平均，以获得分类。 可以提取和利用特征表示来定义可以在字体建议，字体浏览或字体识别应用中使用的字体之间的字体相似性。
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公开(公告)号：US20170236055A1
公开(公告)日：2017-08-17
申请号：US15094633
申请日：2016-04-08
Applicant: ADOBE SYSTEMS INCORPORATED
Inventor： ZHE LIN , XIAOHUI SHEN , JONATHAN BRANDT , JIANMING ZHANG , CHEN FANG
CPC classification number: G06N3/08 , G06F17/30247 , G06N3/0454 , G06N3/0472 , G06N99/005
Abstract: Embodiments of the present invention provide an automated image tagging system that can predict a set of tags, along with relevance scores, that can be used for keyword-based image retrieval, image tag proposal, and image tag auto-completion based on user input. Initially, during training, a clustering technique is utilized to reduce cluster imbalance in the data that is input into a convolutional neural network (CNN) for training feature data. In embodiments, the clustering technique can also be utilized to compute data point similarity that can be utilized for tag propagation (to tag untagged images). During testing, a diversity based voting framework is utilized to overcome user tagging biases. In some embodiments, bigram re-weighting can down-weight a keyword that is likely to be part of a bigram based on a predicted tag set.
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公开(公告)号：US20170004383A1
公开(公告)日：2017-01-05
申请号：US14788113
申请日：2015-06-30
Applicant: ADOBE SYSTEMS INCORPORATED
Inventor： ZHE LIN , JONATHAN BRANDT , XIAOHUI SHEN , JAE-PIL HEO , JIANCHAO YANG
CPC classification number: G06F17/30268 , G06F17/30277 , G06K9/6215 , G06T2207/20084
Abstract: In various implementations, a personal asset management application is configured to perform operations that facilitate the ability to search multiple images, irrespective of the images having characterizing tags associated therewith or without, based on a simple text-based query. A first search is conducted by processing a text-based query to produce a first set of result images used to further generate a visually-based query based on the first set of result images. A second search is conducted employing the visually-based query that was based on the first set of result images received in accordance with the first search conducted and based on the text-based query. The second search can generate a second set of result images, each having visual similarity to at least one of the images generated for the first set of result images.
Abstract translation: 在各种实现中，个人资产管理应用被配置为执行操作，其便于搜索多个图像的能力，而不管基于简单的基于文本的查询，具有与其相关联的或不具有特征标签的图像。 通过处理基于文本的查询以产生用于基于第一组结果图像进一步生成基于视觉的查询的第一组结果图像来进行第一搜索。 使用基于基于根据所进行的第一次搜索接收的第一组结果图像并基于基于文本的查询的基于视觉的查询进行第二搜索。 第二搜索可以产生第二组结果图像，每个结果图像与对于第一组结果图像生成的图像中的至少一个图像具有视觉相似性。
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公开(公告)号：US20150339273A1
公开(公告)日：2015-11-26
申请号：US14286242
申请日：2014-05-23
Applicant: ADOBE SYSTEMS INCORPORATED
Inventor： JIANCHAO YANG , HAILIN JIN , JONATHAN BRANDT
IPC: G06F17/21 , G06F3/0484 , G06F17/30
CPC classification number: G06F17/214 , G06F3/0484 , G06F3/04842 , G06F17/30 , G06F17/30864 , G06F17/30958 , G06T11/206
Abstract: Font graphs are defined having a finite set of nodes representing fonts and a finite set of undirected edges denoting similarities between fonts. The font graphs enable users to browse and identify similar fonts. Indications corresponding to a degree of similarity between connected nodes may be provided. A selection of a desired font or characteristics associated with one or more attributes of the desired font is received from a user interacting with the font graph. The font graph is dynamically redefined based on the selection.
Abstract translation: 字体图被定义为具有表示字体的有限的节点集合和表示字体之间的相似性的无向边的有限集合。 字体图使用户能够浏览和识别类似的字体。 可以提供与连接的节点之间的相似程度相对应的指示。 从与字体图形交互的用户接收与期望字体的一个或多个属性相关联的期望字体或特征的选择。 基于选择动态地重新定义字体图。
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