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公开(公告)号：US09418319B2
公开(公告)日：2016-08-16
申请号：US14550800
申请日：2014-11-21
Applicant: Adobe Systems Incorporated
Inventor： Xiaohui Shen , Haoxiang Li , Zhe Lin , Jonathan W. Brandt
CPC classification number: G06K9/00288 , G06K9/4628 , G06K9/6257 , G06N3/0454
Abstract: Different candidate windows in an image are identified, such as by sliding a rectangular or other geometric shape of different sizes over an image to identify portions of the image (groups of pixels in the image). The candidate windows are analyzed by a set of convolutional neural networks, which are cascaded so that the input of one convolutional neural network layer is based on the input of another convolutional neural network layer. Each convolutional neural network layer drops or rejects one or more candidate windows that the convolutional neural network layer determines does not include an object (e.g., a face). The candidate windows that are identified as including an object (e.g., a face) are analyzed by another one of the convolutional neural network layers. The candidate windows identified by the last of the convolutional neural network layers are the indications of the objects (e.g., faces) in the image.
Abstract translation: 识别图像中的不同候选窗口，例如通过在图像上滑动不同尺寸的矩形或其他几何形状以识别图像（图像中的像素组）的部分。 候选窗口由一组卷积神经网络进行分析，这些网络级联，使得一个卷积神经网络层的输入基于另一个卷积神经网络层的输入。 每个卷积神经网络层丢弃或拒绝卷积神经网络层确定的一个或多个候选窗口不包括对象（例如，面部）。 识别为包括对象（例如脸部）的候选窗口由另一个卷积神经网络层分析。 由最后的卷积神经网络层识别的候选窗口是图像中的对象（例如，面部）的指示。
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12.发明申请Image Classification Using Images with Separate Grayscale and Color Channels 有权使用具有独立灰度和彩色通道的图像的图像分类公开(公告)号：US20150139536A1
公开(公告)日：2015-05-21
申请号：US14081684
申请日：2013-11-15
Applicant: Adobe Systems Incorporated
Inventor： Hailin Jin , Thomas Le Paine , Jianchao Yang , Zhe Lin , Jonathan W. Brandt
CPC classification number: G06K9/6267 , G06K9/46 , G06K9/4652
Abstract: Image classification techniques using images with separate grayscale and color channels are described. In one or more implementations, an image classification network includes grayscale filters and color filters which are separate from the grayscale filters. The grayscale filters are configured to extract grayscale features from a grayscale channel of an image, and the color filters are configured to extract color features from a color channel of the image. The extracted grayscale features and color features are used to identify an object in the image, and the image is classified based on the identified object.
Abstract translation: 描述使用具有单独灰度和颜色通道的图像的图像分类技术。 在一个或多个实现中，图像分类网络包括与灰阶滤波器分离的灰度滤波器和滤色器。 灰度滤波器被配置为从图像的灰度级通道提取灰度特征，并且滤色器被配置为从图像的颜色通道中提取颜色特征。 提取的灰度特征和颜色特征用于识别图像中的对象，并且基于识别的对象对图像进行分类。
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公开(公告)号：US08971668B2
公开(公告)日：2015-03-03
申请号：US13778938
申请日：2013-02-27
Applicant: Adobe Systems Incorporated
Inventor： Jonathan W. Brandt , Nico A. Becherer , Sven Duwenhorst
CPC classification number: G06F17/3025 , G06K9/4652
Abstract: Each of multiple images is analyzed to determine how the colors of the pixels of the image are distributed throughout the color space of the image. Different covariance based characteristics of the image are determined that identify a direction, as well as magnitude in each direction, of the distribution of colors of the image pixels. These different covariance based characteristics that are determined for an image can be saved as associated with the image, allowing the characteristics to be accessed and used as a basis for searching the images to identify particular types of images. These different covariance based characteristics can also be used to order the images identified by a search.
Abstract translation: 分析多个图像中的每一个以确定图像的像素的颜色如何分布在图像的整个颜色空间中。 确定图像的不同协方差特征，其识别图像像素的颜色分布的方向以及每个方向上的大小。 为图像确定的这些不同的协方差特征可以被保存为与图像相关联，允许访问特征并将其用作搜索图像以识别特定类型的图像的基础。 这些不同的协方差特征也可以用于对通过搜索识别的图像进行排序。
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公开(公告)号：US09846840B1
公开(公告)日：2017-12-19
申请号：US15164310
申请日：2016-05-25
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Xiaohui Shen , Jonathan W. Brandt , Jianming Zhang
CPC classification number: G06N3/084 , G06F17/30259
Abstract: Semantic class localization techniques and systems are described. In one or more implementation, a technique is employed to back communicate relevancies of aggregations back through layers of a neural network. Through use of these relevancies, activation relevancy maps are created that describe relevancy of portions of the image to the classification of the image as corresponding to a semantic class. In this way, the semantic class is localized to portions of the image. This may be performed through communication of positive and not negative relevancies, use of contrastive attention maps to different between semantic classes and even within a same semantic class through use of a self-contrastive technique.
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公开(公告)号：US20170132290A1
公开(公告)日：2017-05-11
申请号：US14938752
申请日：2015-11-11
Applicant: Adobe Systems Incorporated
Inventor： Zeke Koch , Gavin Stuart Peter Miller , Jonathan W. Brandt , Nathan A. Carr , Radomir Mech , Walter Wei-Tuh Chang , Scott D. Cohen , Hailin Jin
CPC classification number: G06N3/08 , G06F16/5854 , G06N5/022 , G06N20/00
Abstract: Image search techniques and systems involving emotions are described. In one or more implementations, a digital medium environment of a content sharing service is described for image search result configuration and control based on a search request that indicates an emotion. The search request is received that includes one or more keywords and specifies an emotion. Images are located that are available for licensing by matching one or more tags associated with the image with the one or more keywords and as corresponding to the emotion. The emotion of the images is identified using one or more models that are trained using machine learning based at least in part on training images having tagged emotions. Output is controlled of a search result having one or more representations of the images that are selectable to license respective images from the content sharing service.
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Patent Agency Ranking
公开(公告)号：US20170131876A1
公开(公告)日：2017-05-11
申请号：US14938628
申请日：2015-11-11
Applicant: Adobe Systems Incorporated
Inventor： Zeke Koch , Gavin Stuart Peter Miller , Jonathan W. Brandt , Nathan A. Carr , Radomir Mech , Walter Wei-Tuh Chang , Scott D. Cohen , Hailin Jin
IPC: G06F3/0484 , G06T7/00 , G06F3/0482 , G06F17/27 , G06F17/30 , G06T11/00
CPC classification number: G06T11/001 , G06F17/30244 , G06F17/3025 , G06F17/30268 , G06F17/30867 , G06T11/60
Abstract: Content creation and sharing integration techniques and systems are described. In one or more implementations, techniques are described in which modifiable versions of content (e.g., images) are created and shared via a content sharing service such that image creation functionality used to create the images is preserved to permit continued creation using this functionality. In one or more additional implementations, image creation functionality employed by a creative professional to create content is leveraged to locate similar images from a content sharing service.
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公开(公告)号：US09563825B2
公开(公告)日：2017-02-07
申请号：US14549350
申请日：2014-11-20
Applicant: Adobe Systems Incorporated
Inventor： Xiaohui Shen , Haoxiang Li , Zhe Lin , Jonathan W. Brandt
CPC classification number: G06K9/66 , G06K9/4628 , G06K9/6267 , G06K9/6272 , G06N3/04 , G06N3/0454
Abstract: A convolutional neural network is trained to analyze input data in various different manners. The convolutional neural network includes multiple layers, one of which is a convolution layer that performs a convolution, for each of one or more filters in the convolution layer, of the filter over the input data. The convolution includes generation of an inner product based on the filter and the input data. Both the filter of the convolution layer and the input data are binarized, allowing the inner product to be computed using particular operations that are typically faster than multiplication of floating point values. The possible results for the convolution layer can optionally be pre-computed and stored in a look-up table. Thus, during operation of the convolutional neural network, rather than performing the convolution on the input data, the pre-computed result can be obtained from the look-up table.
Abstract translation: 训练卷积神经网络以各种不同的方式分析输入数据。 卷积神经网络包括多个层，其中之一是卷积层，其对于卷积层中的一个或多个滤波器的每一个，通过输入数据执行滤波器的卷积。 卷积包括基于滤波器和输入数据生成内积。 卷积层的滤波器和输入数据都被二值化，允许使用通常快于浮点值乘法的特定运算来计算内积。 可以可选地预先计算卷积层的可能结果并将其存储在查找表中。 因此，在卷积神经网络的操作期间，不是对输入数据执行卷积，可以从查找表中获得预先计算的结果。
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公开(公告)号：US20160292537A1
公开(公告)日：2016-10-06
申请号：US15183629
申请日：2016-06-15
Applicant: Adobe Systems Incorporated
Inventor： Xiaohui Shen , Zhe Lin , Jonathan W. Brandt
CPC classification number: G06K9/6256 , G06F17/30247 , G06F17/30253 , G06K9/46 , G06K9/4676 , G06K9/469 , G06N5/04 , G06N99/005
Abstract: Feature interpolation techniques are described. In a training stage, features are extracted from a collection of training images and quantized into visual words. Spatial configurations of the visual words in the training images are determined and stored in a spatial configuration database. In an object detection stage, a portion of features of an image are extracted from the image and quantized into visual words. Then, a remaining portion of the features of the image are interpolated using the visual words and the spatial configurations of visual words stored in the spatial configuration database.
Abstract translation: 描述特征插值技术。 在训练阶段，从训练图像的集合中提取特征并量化为视觉词。 训练图像中的视觉词的空间配置被确定并存储在空间配置数据库中。 在物体检测阶段，从图像中提取图像的特征的一部分并量化为视觉词。 然后，使用存储在空间配置数据库中的视觉词和视觉词的空间配置来内插图像的特征的剩余部分。
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公开(公告)号：US09269017B2
公开(公告)日：2016-02-23
申请号：US14081577
申请日：2013-11-15
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Jonathan W. Brandt , Xiaohui Shen , Haoxiang Li
CPC classification number: G06K9/4604 , G06K9/6282 , G06K9/6857
Abstract: Cascaded object detection techniques are described. In one or more implementations, cascaded coarse-to-dense object detection techniques are utilized to detect objects in images. In a first stage, coarse features are extracted from an image, and non-object regions are rejected. Then, in one or more subsequent stages, dense features are extracted from the remaining non-rejected regions of the image to detect one or more objects in the image.
Abstract translation: 描述了级联对象检测技术。 在一个或多个实现中，使用级联的粗到密集对象检测技术来检测图像中的对象。 在第一阶段，从图像中提取粗糙特征，并且拒绝非对象区域。 然后，在一个或多个后续阶段，从图像的剩余未拒绝区域中提取密集特征以检测图像中的一个或多个对象。
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公开(公告)号：US09202138B2
公开(公告)日：2015-12-01
申请号：US13645463
申请日：2012-10-04
Applicant: Adobe Systems Incorporated
Inventor： Jonathan W. Brandt , Zhe Lin , Vuong Le , Lubomir D. Bourdev
CPC classification number: G06K9/6209 , G06K9/00228 , G06K9/2081 , G06K2009/366
Abstract: Various embodiments of methods and apparatus for feature point localization are disclosed. A profile model and a shape model may be applied to an object in an image to determine locations of feature points for each object component. Input may be received to move one of the feature points to a fixed location. Other ones of the feature points may be automatically adjusted to different locations based on the moved feature point.
Abstract translation: 公开了用于特征点定位的方法和装置的各种实施例。 轮廓模型和形状模型可以应用于图像中的对象，以确定每个对象分量的特征点的位置。 可以接收输入以将特征点中的一个移动到固定位置。 其他特征点可以根据所移动的特征点自动调整到不同的位置。
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