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公开(公告)号：US20180137892A1
公开(公告)日：2018-05-17
申请号：US15353186
申请日：2016-11-16
Applicant: Adobe Systems Incorporated
Inventor： Zhihong Ding , Zhe Lin , Xiaohui Shen , Michael Kaplan , Jonathan Brandt
CPC classification number: G11B27/11 , G06K9/00744 , G06K9/3241 , G06T7/97 , G06T11/60 , G06T2207/10016 , G11B27/031
Abstract: The present disclosure is directed toward systems and methods for tracking objects in videos. For example, one or more embodiments described herein utilize various tracking methods in combination with an image search index made up of still video frames indexed from a video. One or more embodiments described herein utilize a backward and forward tracking method that is anchored by one or more key frames in order to accurately track an object through the frames of a video, even when the video is long and may include challenging conditions.
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公开(公告)号：US09569213B1
公开(公告)日：2017-02-14
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到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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13.发明授权公开(公告)号：US09501724B1
公开(公告)日：2016-11-22
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：US09141885B2
公开(公告)日：2015-09-22
申请号：US13953394
申请日：2013-07-29
Applicant: Adobe Systems Incorporated
Inventor： Jianchao Yang , Guang Chen , Jonathan Brandt , Hailin Jin , Elya Shechtman , Aseem Omprakash Agarwala
CPC classification number: G06K9/627 , G06K9/4642
Abstract: A system may be configured as an image recognition machine that utilizes an image feature representation called local feature embedding (LFE). LFE enables generation of a feature vector that captures salient visual properties of an image to address both the fine-grained aspects and the coarse-grained aspects of recognizing a visual pattern depicted in the image. Configured to utilize image feature vectors with LFE, the system may implement a nearest class mean (NCM) classifier, as well as a scalable recognition algorithm with metric learning and max margin template selection. Accordingly, the system may be updated to accommodate new classes with very little added computational cost. This may have the effect of enabling the system to readily handle open-ended image classification problems.
Abstract translation: 系统可以被配置为利用称为局部特征嵌入（LFE）的图像特征表示的图像识别机器。 LFE能够生成捕获图像的显着视觉特性的特征向量，以解决识别图像中描绘的视觉图案的细粒度方面和粗粒度方面。 配置为利用具有LFE的图像特征向量，系统可以实现最近的等级均值（NCM）分类器，以及具有度量学习和最大边距模板选择的可缩放识别算法。 因此，可以更新系统以容纳新类别，而且增加了很少的计算成本。 这可能具有使系统能够容易地处理开放式图像分类问题的效果。
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公开(公告)号：US20140153819A1
公开(公告)日：2014-06-05
申请号：US13691190
申请日：2012-11-30
Applicant: ADOBE SYSTEMS INCORPORATED
Inventor： Zhe Lin , Xin Lu , Jonathan Brandt , Hailin Jin
IPC: G06T5/50
CPC classification number: G06T5/002 , G06T2207/20081 , G06T2207/20084
Abstract: Systems and methods are provided for providing learned, piece-wise patch regression for image enhancement. In one embodiment, an image manipulation application generates training patch pairs that include training input patches and training output patches. Each training patch pair includes a respective training input patch from a training input image and a respective training output patch from a training output image. The training input image and the training output image include at least some of the same image content. The image manipulation application determines patch-pair functions from at least some of the training patch pairs. Each patch-pair function corresponds to a modification to a respective training input patch to generate a respective training output patch. The image manipulation application receives an input image generates an output image from the input image by applying at least some of the patch-pair functions based on at least some input patches of the input image.
Abstract translation: 提供了系统和方法，用于为图像增强提供学习的分段补丁回归。 在一个实施例中，图像处理应用产生训练补丁对，其包括训练输入补丁和训练输出补丁。 每个训练补丁对包括来自训练输入图像的相应训练输入补丁和来自训练输出图像的相应训练输出补丁。 训练输入图像和训练输出图像包括至少一些相同的图像内容。 图像处理应用程序从至少一些训练补丁对确定补丁对功能。 每个补丁对功能对应于对相应的训练输入补丁的修改以生成相应的训练输出补丁。 图像处理应用程序接收输入图像，基于输入图像的至少一些输入图像块，通过应用至少一些补丁对功能，从输入图像生成输出图像。
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Patent Agency Ranking
公开(公告)号：US10140261B2
公开(公告)日：2018-11-27
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 , G06T11/20
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.
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公开(公告)号：US10042866B2
公开(公告)日：2018-08-07
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
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.
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公开(公告)号：US09940100B2
公开(公告)日：2018-04-10
申请号：US14473104
申请日：2014-08-29
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Jonathan Brandt , Xiaohui Shen , Jae-Pil Heo
CPC classification number: G06F7/24 , G06F17/30268 , G06F17/30271 , G06F17/30622
Abstract: Techniques are disclosed for indexing and searching high-dimensional data using inverted file structures and product quantization encoding. An image descriptor is quantized using a form of product quantization to determine which of several inverted lists the image descriptor is to be stored. The image descriptor is appended to the corresponding inverted list with a compact coding using a product quantization encoding scheme. When processing a query, a shortlist is computed that includes a set of candidate search results. The shortlist is based on the orthogonality between two random vectors in high-dimensional spaces. The inverted lists are traversed in the order of the distance between the query and the centroid of a coarse quantizer corresponding to each inverted list. The shortlist is ranked according to the distance estimated by a form of product quantization, and the top images referred to by the ranked shortlist are reported as the search results.
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公开(公告)号：US09607014B2
公开(公告)日：2017-03-28
申请号：US14068238
申请日：2013-10-31
Applicant: Adobe Systems Incorporated
Inventor： Zhaowen Wang , Jianchao Yang , Zhe Lin , Jonathan Brandt
CPC classification number: G06F17/30265 , G06K9/6263 , G06K2209/27
Abstract: A system is configured to annotate an image with tags. As configured, the system accesses an image and generates a set of vectors for the image. The set of vectors may be generated by mathematically transforming the image, such as by applying a mathematical transform to predetermined regions of the image. The system may then query a database of tagged images by submitting the set of vectors as search criteria to a search engine. The querying of the database may obtain a set of tagged images. Next, the system may rank the obtained set of tagged images according to similarity scores that quantify degrees of similarity between the image and each tagged image obtained. Tags from a top-ranked subset of the tagged images may be extracted by the system, which may then annotate the image with these extracted tags.
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公开(公告)号：US20150146973A1
公开(公告)日：2015-05-28
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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