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    Learning image categorization using related attributes
    163.
    发明授权
    Learning image categorization using related attributes 有权

    公开(公告)号：US09953425B2

    公开(公告)日：2018-04-24

    申请号：US14447296

    申请日：2014-07-30

    Applicant: ADOBE SYSTEMS INCORPORATED

    Inventor： Zhe Lin ,  Hailin Jin ,  Jianchao Yang

    IPC: G06N3/04 ,  G06T7/00 ,  G06K9/62

    CPC classification number: G06T7/33 ,  G06K9/627 ,  G06N3/0454

    Abstract: A first set of attributes (e.g., style) is generated through pre-trained single column neural networks and leveraged to regularize the training process of a regularized double-column convolutional neural network (RDCNN). Parameters of the first column (e.g., style) of the RDCNN are fixed during RDCNN training. Parameters of the second column (e.g., aesthetics) are fine-tuned while training the RDCNN and the learning process is supervised by the label identified by the second column (e.g., aesthetics). Thus, features of the images may be leveraged to boost classification accuracy of other features by learning a RDCNN.

    Image color and tone style transfer
    165.
    发明授权
    Image color and tone style transfer 有权

    公开(公告)号：US09857953B2

    公开(公告)日：2018-01-02

    申请号：US14944019

    申请日：2015-11-17

    Applicant: Adobe Systems Incorporated

    Inventor： Kalyan K. Sunkavalli ,  Zhe Lin ,  Xiaohui Shen ,  Joon-Young Lee

    IPC: G06F3/0484 ,  G06F3/0488 ,  G06T11/00

    CPC classification number: G06F3/04845 ,  G06F3/04842 ,  G06F3/0488 ,  G06T11/001

    Abstract: In embodiments of image color and tone style transfer, a computing device implements an image style transfer algorithm to generate a modified image from an input image based on a color style and a tone style of a style image. A user can select the input image that includes color features, as well as select the style image that includes an example of the color style and the tone style to transfer to the input image. A chrominance transfer function can then be applied to transfer the color style to the input image, utilizing a covariance of an input image color of the input image to control modification of the input image color. A luminance transfer function can also be applied to transfer the tone style to the input image, utilizing a tone mapping curve based on a non-linear optimization to estimate luminance parameters of the tone mapping curve.

    Imaging Process Initialization Techniques
    166.
    发明申请
    Imaging Process Initialization Techniques 有权

    公开(公告)号：US20170372493A1

    公开(公告)日：2017-12-28

    申请号：US15191141

    申请日：2016-06-23

    Applicant: Adobe Systems Incorporated

    Inventor： Xin Lu ,  Zhe Lin

    IPC: G06T7/90 ,  G06K9/20 ,  G06T5/00

    CPC classification number: G06T7/90 ,  G06K9/2054 ,  G06K9/4652 ,  G06T5/005 ,  G06T2207/10024 ,  H04N1/622

    Abstract: Imaging process initialization techniques are described. In an implementation, a color estimate is generated for a plurality of pixels within a region of an image. A plurality of pixels outside of the regions are first identified for each pixel of the plurality of pixels within the region. This may include identification of pixels disposed at opposing directions from the pixel being estimated. A color estimate is determined for each of the plurality of pixels based on the identified pixels. As part of this, a weighting may be employed, such as based on a respective distance of each of the pixels outside of the region to the pixel within the region, a distance along the opposing direction for corresponding pixels outside of the region (e.g., at horizontal or vertical directions), and so forth. The color estimate is then used to initialize an imaging process technique.

    GENERATING IMAGE FEATURES BASED ON ROBUST FEATURE-LEARNING
    167.
    发明申请
    GENERATING IMAGE FEATURES BASED ON ROBUST FEATURE-LEARNING 有权

    公开(公告)号：US20170344848A1

    公开(公告)日：2017-11-30

    申请号：US15166164

    申请日：2016-05-26

    Applicant: Adobe Systems Incorporated

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

    IPC: G06K9/46 ,  G06K9/62 ,  G06K9/48 ,  G06N3/08 ,  G06N3/04

    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.

    Modeling Semantic Concepts in an Embedding Space as Distributions
    169.
    发明申请
    Modeling Semantic Concepts in an Embedding Space as Distributions 审中-公开

    公开(公告)号：US20170206465A1

    公开(公告)日：2017-07-20

    申请号：US14996959

    申请日：2016-01-15

    Applicant: Adobe Systems Incorporated

    Inventor： Hailin Jin ,  Zhou Ren ,  Zhe Lin ,  Chen Fang

    IPC: G06N99/00 ,  G06N3/08 ,  G06F17/28 ,  G06F17/30

    CPC classification number: G06N20/00 ,  G06F16/5866 ,  G06F17/241 ,  G06F17/2785 ,  G06N3/0454 ,  G06N3/08

    Abstract: Modeling semantic concepts in an embedding space as distributions is described. In the embedding space, both images and text labels are represented. The text labels describe semantic concepts that are exhibited in image content. In the embedding space, the semantic concepts described by the text labels are modeled as distributions. By using distributions, each semantic concept is modeled as a continuous cluster which can overlap other clusters that model other semantic concepts. For example, a distribution for the semantic concept “apple” can overlap distributions for the semantic concepts “fruit” and “tree” since can refer to both a fruit and a tree. In contrast to using distributions, conventionally configured visual-semantic embedding spaces represent a semantic concept as a single point. Thus, unlike these conventionally configured embedding spaces, the embedding spaces described herein are generated to model semantic concepts as distributions, such as Gaussian distributions, Gaussian mixtures, and so on.

    Enhancement of Skin, Including Faces, in Photographs
    170.
    发明申请
    Enhancement of Skin, Including Faces, in Photographs 有权

    公开(公告)号：US20170132459A1

    公开(公告)日：2017-05-11

    申请号：US14938568

    申请日：2015-11-11

    Applicant: Adobe Systems Incorporated

    Inventor： Kalyan K. Sunkavalli ,  Zhe Lin ,  Xiaohui Shen ,  Joon-Young Lee

    IPC: G06K9/00 ,  G06T5/00

    CPC classification number: G06K9/00288 ,  G06K9/00228 ,  G06K9/00234 ,  G06T5/001 ,  G06T5/008 ,  G06T2207/10024 ,  G06T2207/20032 ,  G06T2207/20208 ,  G06T2207/30201

    Abstract: An image processing application performs improved face exposure correction on an input image. The image processing application receives an input image having a face and ascertains a median luminance associated with a face region corresponding to the face. The image processing application determines whether the median luminance is less than a threshold luminance. If the median luminance is less than the threshold luminance, the application computes weights based on a spatial distance parameter and a similarity parameter associated with the median chrominance of the face region. The image processing application then computes a corrected luminance using the weights and applies the corrected luminance to the input image. The image processing application can also perform improved face color correction by utilizing stylization-induced shifts in skin tone color to control how aggressively stylization is applied to an image.

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