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公开(公告)号：US09002116B2
公开(公告)日：2015-04-07
申请号：US13782181
申请日：2013-03-01
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Jonathan Brandt , Xiaohui Shen
CPC classification number: G06K9/6202 , G06K9/00228 , G06K2009/00328
Abstract: One exemplary embodiment involves identifying feature matches between each of a plurality of object images and a test image, each feature matches between a feature of a respective object image and a matching feature of the test image, wherein there is a spatial relationship between each respective object image feature and a test image feature, and wherein the object depicted in the test image comprises a plurality of attributes. Additionally, the embodiment involves estimating, for each attribute in the test image, an attribute value based at least in part on information stored in a metadata associated with each of the object images.
Abstract translation: 一个示例性实施例涉及识别多个对象图像和测试图像中的每一个之间的特征匹配，每个特征在相应对象图像的特征与测试图像的匹配特征之间匹配，其中每个相应对象之间存在空间关系 图像特征和测试图像特征，并且其中测试图像中描绘的对象包括多个属性。 另外，该实施例涉及至少部分地基于存储在与每个对象图像相关联的元数据中的信息来估计测试图像中的每个属性的属性值。
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公开(公告)号：US20150030238A1
公开(公告)日：2015-01-29
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
IPC: G06K9/62
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：US20140247993A1
公开(公告)日：2014-09-04
申请号：US13782804
申请日：2013-03-01
Applicant: ADOBE SYSTEMS INCORPORATED
Inventor： Zhe Lin , Jonathan Brandt , Xiaohui Shen
IPC: G06T7/00
CPC classification number: G06T7/0079 , G06K9/00281
Abstract: One exemplary embodiment involves identifying feature matches between each of a plurality of object images and a test image, each of the feature matches between a feature of a respective object image and a matching feature of the test image, wherein there is a spatial relationship between each respective object image feature and a first landmark of the object image, the first landmark at a known location in the object image. The embodiment additionally involves estimating a plurality of locations for a second landmark for the test image, the estimated locations based at least in part on the feature matches and the spatial relationships, and estimating a final location for the second landmark from the plurality of locations for the second landmark for the test image.
Abstract translation: 一个示例性实施例涉及识别多个对象图像中的每一个与测试图像之间的特征匹配，每个特征在相应对象图像的特征与测试图像的匹配特征之间匹配，其中每个对象图像之间存在空间关系 对象图像的相应对象图像特征和第一界标，在对象图像中的已知位置处的第一界标。 该实施例另外包括估计用于测试图像的第二地标的多个位置，至少部分地基于特征匹配和空间关系估计位置，以及从多个位置估计第二地标的最终位置， 测试图像的第二个里程碑。
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公开(公告)号：US20140247963A1
公开(公告)日：2014-09-04
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：US20130282712A1
公开(公告)日：2013-10-24
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.
Abstract translation: 图像搜索方法包括接收第一查询，第一查询提供第一图像约束。 响应于第一查询，执行多个图像的第一次搜索以识别满足第一约束的第一组图像。 呈现第一搜索结果，其包括被识别为满足第一约束的第一组图像。 接收第二查询，第二查询参照第一组图像的第一图像提供第二图像约束。 响应于第二查询执行多个图像的第二搜索，以识别满足第二约束的第二组图像。 呈现第二搜索结果，其包括被识别为满足第二约束的第二组图像。
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Patent Agency Ranking
公开(公告)号：US10068129B2
公开(公告)日：2018-09-04
申请号：US14945198
申请日：2015-11-18
Applicant: Adobe Systems Incorporated
Inventor： Jonathan Brandt , Zhe Lin , Xiaohui Shen , Haoxiang Li
Abstract: Methods and systems for recognizing people in images with increased accuracy are disclosed. In particular, the methods and systems divide images into a plurality of clusters based on common characteristics of the images. The methods and systems also determine an image cluster to which an image with an unknown person instance most corresponds. One or more embodiments determine a probability that the unknown person instance is each known person instance in the image cluster using a trained cluster classifier of the image cluster. Optionally, the methods and systems determine context weights for each combination of an unknown person instance and each known person instance using a conditional random field algorithm based on a plurality of context cues associated with the unknown person instance and the known person instances. The methods and systems calculate a contextual probability based on the cluster-based probabilities and context weights to identify the unknown person instance.
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27.发明申请公开(公告)号：US20180121768A1
公开(公告)日：2018-05-03
申请号：US15429769
申请日：2017-02-10
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Mai Long , Jonathan Brandt , Hailin Jin , Chen Fang
CPC classification number: G06K9/66 , G06F16/532 , G06K9/4628 , G06K9/52 , G06K9/6215 , G06K9/6256 , G06K9/6267 , G06K9/6271 , G06K9/726 , G06K2009/4666 , G06N3/0454 , G06N3/08
Abstract: The present disclosure includes methods and systems for searching for digital visual media based on semantic and spatial information. In particular, one or more embodiments of the disclosed systems and methods identify digital visual media displaying targeted visual content in a targeted region based on a query term and a query area provide via a digital canvas. Specifically, the disclosed systems and methods can receive user input of a query term and a query area and provide the query term and query area to a query neural network to generate a query feature set. Moreover, the disclosed systems and methods can compare the query feature set to digital visual media feature sets. Further, based on the comparison, the disclosed systems and methods can identify digital visual media portraying targeted visual content corresponding to the query term within a targeted region corresponding to the query area.
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28.发明申请公开(公告)号：US20170364773A1
公开(公告)日：2017-12-21
申请号：US15680282
申请日：2017-08-18
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.
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29.发明授权公开(公告)号：US09767386B2
公开(公告)日：2017-09-19
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.
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公开(公告)号：US20170140213A1
公开(公告)日：2017-05-18
申请号：US14945198
申请日：2015-11-18
Applicant: Adobe Systems Incorporated
Inventor： Jonathan Brandt , Zhe Lin , Xiaohui Shen , Haoxiang Li
CPC classification number: G06K9/00295 , G06K9/00369 , G06K9/00677 , G06K9/6218 , G06K9/66
Abstract: Methods and systems for recognizing people in images with increased accuracy are disclosed. In particular, the methods and systems divide images into a plurality of clusters based on common characteristics of the images. The methods and systems also determine an image cluster to which an image with an unknown person instance most corresponds. One or more embodiments determine a probability that the unknown person instance is each known person instance in the image cluster using a trained cluster classifier of the image cluster. Optionally, the methods and systems determine context weights for each combination of an unknown person instance and each known person instance using a conditional random field algorithm based on a plurality of context cues associated with the unknown person instance and the known person instances. The methods and systems calculate a contextual probability based on the cluster-based probabilities and context weights to identify the unknown person instance.
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