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    Content sharing collections and navigation
    31.
    发明授权
    Content sharing collections and navigation 有权

    公开(公告)号：US10198590B2

    公开(公告)日：2019-02-05

    申请号：US14938724

    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: G06F21/62 ,  G06F3/0484 ,  G06F17/30

    Abstract: Content creation collection and navigation techniques and systems are described. In one example, a representative image is used by a content sharing service to interact with a collection of images provided as part of a search result. In another example, a user interface image navigation control is configured to support user navigation through images based on one or more metrics. In a further example, a user interface image navigation control is configured to support user navigation through images based on one or more metrics identified for an object selected from the image. In yet another example, collections of images are leveraged as part of content creation. In another example, data obtained from a content sharing service is leveraged to indicate suitability of images of a user for licensing as part of the service.

    Accelerating object detection
    32.
    发明授权
    Accelerating object detection 有权

    公开(公告)号：US10043057B2

    公开(公告)日：2018-08-07

    申请号：US15254587

    申请日：2016-09-01

    Applicant: Adobe Systems Incorporated

    Inventor： Xiaohui Shen ,  Zhe Lin ,  Jonathan W. Brandt

    IPC: G06K9/00 ,  G06K9/62 ,  G06K9/46

    Abstract: Accelerating object detection techniques are described. In one or more implementations, adaptive sampling techniques are used to extract features from an image. Coarse features are extracted from the image and used to generate an object probability map. Then, dense features are extracted from high-probability object regions of the image identified in the object probability map to enable detection of an object in the image. In one or more implementations, cascade object detection techniques are used to detect an object in an image. In a first stage, exemplars in a first subset of exemplars are applied to features extracted from the multiple regions of the image to detect object candidate regions. Then, in one or more validation stages, the object candidate regions are validated by applying exemplars from the first subset of exemplars and one or more additional subsets of exemplars.

    SEMANTIC CLASS LOCALIZATION IN IMAGES
    34.
    发明申请
    SEMANTIC CLASS LOCALIZATION IN IMAGES 有权

    公开(公告)号：US20170344884A1

    公开(公告)日：2017-11-30

    申请号：US15164310

    申请日：2016-05-25

    Applicant: Adobe Systems Incorporated

    Inventor： Zhe Lin ,  Xiaohui Shen ,  Jonathan W. Brandt ,  Jianming Zhang

    IPC: G06N3/08 ,  G06F17/30

    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.

    Object detection using cascaded convolutional neural networks
    35.
    发明授权
    Object detection using cascaded convolutional neural networks 有权

    公开(公告)号：US09697416B2

    公开(公告)日：2017-07-04

    申请号：US15196478

    申请日：2016-06-29

    Applicant: Adobe Systems Incorporated

    Inventor： Xiaohui Shen ,  Haoxiang Li ,  Zhe Lin ,  Jonathan W. Brandt

    IPC: G06K9/62 ,  G06K9/00 ,  G06K9/46

    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.

    Feature interpolation
    38.
    发明授权
    Feature interpolation 有权
    特征插值

    公开(公告)号：US09424484B2

    公开(公告)日：2016-08-23

    申请号：US14335059

    申请日：2014-07-18

    Applicant: Adobe Systems Incorporated

    Inventor： Xiaohui Shen ,  Zhe Lin ,  Jonathan W. Brandt

    IPC: G06K9/00 ,  G06K9/46 ,  G06K9/62 ,  G06F17/30

    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。

    Image classification using images with separate grayscale and color channels
    39.
    发明授权
    Image classification using images with separate grayscale and color channels 有权
    使用具有单独灰度和颜色通道的图像的图像分类

    公开(公告)号：US09230192B2

    公开(公告)日：2016-01-05

    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

    IPC: G06K9/52 ,  G06K9/62 ,  G06K9/46

    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。

    Cascaded Object Detection
    40.
    发明申请
    Cascaded Object Detection 有权
    级联对象检测

    公开(公告)号：US20150139551A1

    公开(公告)日：2015-05-21

    申请号：US14081577

    申请日：2013-11-15

    Applicant: Adobe Systems Incorporated

    Inventor： Zhe Lin ,  Jonathan W. Brandt ,  Xiaohui Shen ,  Haoxiang Li

    IPC: G06K9/46 ,  G06K9/62

    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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