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公开(公告)号：US09117262B2
公开(公告)日：2015-08-25
申请号：US13691190
申请日：2012-11-30
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Xin Lu , Jonathan Brandt , Hailin Jin
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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公开(公告)号：US09081800B2
公开(公告)日：2015-07-14
申请号：US13781988
申请日：2013-03-01
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Jonathan Brandt , Xiaohui Shen
CPC classification number: G06F17/30259 , G06K9/00228 , G06K9/4676 , G06K9/6211 , G06K9/6292
Abstract: One exemplary embodiment involves receiving a test image generating, by a plurality of maps for the test image based on a plurality of object images. Each of the object images comprises an object of a same object type, e.g., each comprising a different face. Each of the plurality of maps is generated to provide information about the similarity of at least a portion of a respective object image to each of a plurality of portions of the test image. The exemplary embodiment further comprises detecting a test image object within the test image based at least in part on the plurality of maps.
Abstract translation: 一个示例性实施例涉及通过基于多个对象图像的测试图像的多个映射来接收测试图像。 每个对象图像包括相同对象类型的对象，例如，每个对象包括不同的面。 生成多个地图中的每一个以提供关于相应对象图像的至少一部分与测试图像的多个部分中的每一个相似度的信息。 该示例性实施例还包括至少部分地基于多个地图检测测试图像内的测试图像对象。
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公开(公告)号：US20140247996A1
公开(公告)日：2014-09-04
申请号：US13781988
申请日：2013-03-01
Applicant: ADOBE SYSTEMS INCORPORATED
Inventor： Zhe Lin , Jonathan Brandt , Xiaohui Shen
IPC: G06K9/62
CPC classification number: G06F17/30259 , G06K9/00228 , G06K9/4676 , G06K9/6211 , G06K9/6292
Abstract: One exemplary embodiment involves receiving a test image generating, by a plurality of maps for the test image based on a plurality of object images. Each of the object images comprises an object of a same object type, e.g., each comprising a different face. Each of the plurality of maps is generated to provide information about the similarity of at least a portion of a respective object image to each of a plurality of portions of the test image. The exemplary embodiment further comprises detecting a test image object within the test image based at least in part on the plurality of maps.
Abstract translation: 一个示例性实施例涉及通过基于多个对象图像的测试图像的多个映射来接收测试图像。 每个对象图像包括相同对象类型的对象，例如，每个对象包括不同的面。 生成多个地图中的每一个以提供关于相应对象图像的至少一部分与测试图像的多个部分中的每一个相似度的信息。 该示例性实施例还包括至少部分地基于多个地图检测测试图像内的测试图像对象。
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公开(公告)号：US20140153817A1
公开(公告)日：2014-06-05
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配。 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：US10235623B2
公开(公告)日：2019-03-19
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
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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Patent Agency Ranking
公开(公告)号：US20180260655A1
公开(公告)日：2018-09-13
申请号：US15979546
申请日：2018-05-15
Applicant: Adobe Systems Incorporated
Inventor： Jianchao Yang , Jonathan Brandt
CPC classification number: G06K9/52 , G06F16/56 , G06K9/46 , G06K9/4642 , G06K9/6201 , G06K9/621 , G06K9/6226 , G06K9/6267 , G06K2009/4666
Abstract: Techniques are disclosed for image feature representation. The techniques exhibit discriminative power that can be used in any number of classification tasks, and are particularly effective with respect to fine-grained image classification tasks. In an embodiment, a given image to be classified is divided into image patches. A vector is generated for each image patch. Each image patch vector is compared to the Gaussian mixture components (each mixture component is also a vector) of a Gaussian Mixture Model (GMM). Each such comparison generates a similarity score for each image patch vector. For each Gaussian mixture component, the image patch vectors associated with a similarity score that is too low are eliminated. The selectively pooled vectors from all the Gaussian mixture components are then concatenated to form the final image feature vector, which can be provided to a classifier so the given input image can be properly categorized.
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公开(公告)号：US09990558B2
公开(公告)日：2018-06-05
申请号：US15705151
申请日：2017-09-14
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Xiaohui Shen , Jonathan Brandt , Jianming Zhang
CPC classification number: G06K9/4628 , G06K9/6298 , G06K2009/00322 , G06N3/04 , G06N3/08
Abstract: Techniques for increasing robustness of a convolutional neural network based on training that uses multiple datasets and multiple tasks are described. For example, a computer system trains the convolutional neural network across multiple datasets and multiple tasks. The convolutional neural network is configured for learning features from images and accordingly generating feature vectors. By using multiple datasets and multiple tasks, the robustness of the convolutional neural network is increased. A feature vector of an image is used to apply an image-related operation to the image. For example, the image is classified, indexed, or objects in the image are tagged based on the feature vector. Because the robustness is increased, the accuracy of the generating feature vectors is also increased. Hence, the overall quality of an image service is enhanced, where the image service relies on the image-related operation.
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公开(公告)号：US20180005070A1
公开(公告)日：2018-01-04
申请号：US15705151
申请日：2017-09-14
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Xiaohui Shen , Jonathan Brandt , Jianming Zhang
CPC classification number: G06K9/4628 , G06K9/6298 , G06K2009/00322 , G06N3/04 , G06N3/08
Abstract: Techniques for increasing robustness of a convolutional neural network based on training that uses multiple datasets and multiple tasks are described. For example, a computer system trains the convolutional neural network across multiple datasets and multiple tasks. The convolutional neural network is configured for learning features from images and accordingly generating feature vectors. By using multiple datasets and multiple tasks, the robustness of the convolutional neural network is increased. A feature vector of an image is used to apply an image-related operation to the image. For example, the image is classified, indexed, or objects in the image are tagged based on the feature vector. Because the robustness is increased, the accuracy of the generating feature vectors is also increased. Hence, the overall quality of an image service is enhanced, where the image service relies on the image-related operation.
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公开(公告)号：US09830526B1
公开(公告)日：2017-11-28
申请号：US15166164
申请日：2016-05-26
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Xiaohui Shen , Jonathan Brandt , Jianming Zhang
CPC classification number: G06K9/4628 , G06K9/6298 , G06K2009/00322 , G06N3/04 , G06N3/08
Abstract: Techniques for increasing robustness of a convolutional neural network based on training that uses multiple datasets and multiple tasks are described. For example, a computer system trains the convolutional neural network across multiple datasets and multiple tasks. The convolutional neural network is configured for learning features from images and accordingly generating feature vectors. By using multiple datasets and multiple tasks, the robustness of the convolutional neural network is increased. A feature vector of an image is used to apply an image-related operation to the image. For example, the image is classified, indexed, or objects in the image are tagged based on the feature vector. Because the robustness is increased, the accuracy of the generating feature vectors is also increased. Hence, the overall quality of an image service is enhanced, where the image service relies on the image-related operation.
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40.发明申请TRAINING A CLASSIFIER ALGORITHM USED FOR AUTOMATICALLY GENERATING TAGS TO BE APPLIED TO IMAGES 有权训练用于自动生成要应用于图像的标签的分类器算法公开(公告)号：US20160379091A1
公开(公告)日：2016-12-29
申请号：US14747877
申请日：2015-06-23
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Stefan Guggisberg , Jonathan Brandt , Michael Marth
CPC classification number: G06K9/6256 , G06K9/00724 , G06K9/6262
Abstract: This disclosure relates to training a classifier algorithm that can be used for automatically selecting tags to be applied to a received image. For example, a computing device can group training images together based on the training images having similar tags. The computing device trains a classifier algorithm to identify the training images as semantically similar to one another based on the training images being grouped together. The trained classifier algorithm is used to determine that an input image is semantically similar to an example tagged image. A tag is generated for the input image using tag content from the example tagged image based on determining that the input image is semantically similar to the tagged image.
Abstract translation: 本公开涉及训练可用于自动选择要应用于接收到的图像的标签的分类器算法。 例如，计算设备可以基于具有相似标签的训练图像将训练图像组合在一起。 计算设备训练分类器算法，以基于训练图像被分组在一起来将训练图像识别为语义上彼此相似。 训练分类器算法用于确定输入图像在语义上类似于示例标记图像。 基于确定输入图像在语义上类似于带标签的图像，使用来自示例标记图像的标签内容为输入图像生成标签。
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