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    Learning copy space using regression and segmentation neural networks
    1.
    发明授权
    Learning copy space using regression and segmentation neural networks 有权

    公开(公告)号：US11605168B2

    公开(公告)日：2023-03-14

    申请号：US17215067

    申请日：2021-03-29

    Applicant: Adobe Inc.

    Inventor： Mingyang Ling ,  Alex Filipkowski ,  Zhe Lin ,  Jianming Zhang ,  Samarth Gulati

    IPC: G06K9/62 ,  G06T7/11 ,  G06T7/136 ,  G06T7/143 ,  G06T7/174 ,  G06F18/214 ,  G06N3/045 ,  G06V10/25 ,  G06V10/764 ,  G06V10/82 ,  G06V10/26

    Abstract: Techniques are disclosed for characterizing and defining the location of a copy space in an image. A methodology implementing the techniques according to an embodiment includes applying a regression convolutional neural network (CNN) to an image. The regression CNN is configured to predict properties of the copy space such as size and type (natural or manufactured). The prediction is conditioned on a determination of the presence of the copy space in the image. The method further includes applying a segmentation CNN to the image. The segmentation CNN is configured to generate one or more pixel-level masks to define the location of copy spaces in the image, whether natural or manufactured, or to define the location of a background region of the image. The segmentation CNN may include a first stage comprising convolutional layers and a second stage comprising pairs of boundary refinement layers and bilinear up-sampling layers.

    LEARNING COPY SPACE USING REGRESSION AND SEGMENTATION NEURAL NETWORKS
    4.
    发明申请
    LEARNING COPY SPACE USING REGRESSION AND SEGMENTATION NEURAL NETWORKS 审中-公开

    公开(公告)号：US20200160111A1

    公开(公告)日：2020-05-21

    申请号：US16191724

    申请日：2018-11-15

    Applicant: ADOBE INC.

    Inventor： Mingyang Ling ,  Alex Filipkowski ,  Zhe Lin ,  Jianming Zhang ,  Samarth Gulati

    IPC: G06K9/62 ,  G06T7/136 ,  G06T7/174 ,  G06N3/04 ,  G06T7/143

    Abstract: Techniques are disclosed for characterizing and defining the location of a copy space in an image. A methodology implementing the techniques according to an embodiment includes applying a regression convolutional neural network (CNN) to an image. The regression CNN is configured to predict properties of the copy space such as size and type (natural or manufactured). The prediction is conditioned on a determination of the presence of the copy space in the image. The method further includes applying a segmentation CNN to the image. The segmentation CNN is configured to generate one or more pixel-level masks to define the location of copy spaces in the image, whether natural or manufactured, or to define the location of a background region of the image. The segmentation CNN may include a first stage comprising convolutional layers and a second stage comprising pairs of boundary refinement layers and bilinear up-sampling layers.

    IMAGE SEARCHING BY EMPLOYING LAYERED SEARCH CONSTRAINTS
    5.
    发明申请
    IMAGE SEARCHING BY EMPLOYING LAYERED SEARCH CONSTRAINTS 审中-公开

    公开(公告)号：US20190163766A1

    公开(公告)日：2019-05-30

    申请号：US15824836

    申请日：2017-11-28

    Applicant: ADOBE INC.

    Inventor： Samarth Gulati ,  Brett Butterfield ,  Baldo Faieta ,  Bernard James Kerr ,  Kent Andrew Edmonds

    IPC: G06F17/30

    Abstract: Systems and methods for searching digital content, such as digital images, are disclosed. A method includes receiving a first search constraint and generating search results based on the first search constraint. A search constraint includes search values or criteria. The search results include a ranked set of digital images. A second search constraint and a weight value associated with the second search constraint are received. The search results are updated based on the second search constraint and the weight value. The updated search results are provided to a user. Updating the search results includes determining a ranking (or a re-ranking) for each item of content included in the search results based on the first search constraint, the second search constraint, and the weight value. Re-ranking the search results may further be based on a weight value associated with the first search constraint, such as a default or maximum weight value.

    Image searching by employing layered search constraints
    8.
    发明授权
    Image searching by employing layered search constraints 有权

    公开(公告)号：US11030236B2

    公开(公告)日：2021-06-08

    申请号：US15824836

    申请日：2017-11-28

    Applicant: ADOBE INC.

    Inventor： Samarth Gulati ,  Brett Butterfield ,  Baldo Faieta ,  Bernard James Kerr ,  Kent Andrew Edmonds

    IPC: G06F16/583 ,  G06F16/532 ,  G06F16/56 ,  G06F16/9535

    Abstract: Systems and methods for searching digital content, such as digital images, are disclosed. A method includes receiving a first search constraint and generating search results based on the first search constraint. A search constraint includes search values or criteria. The search results include a ranked set of digital images. A second search constraint and a weight value associated with the second search constraint are received. The search results are updated based on the second search constraint and the weight value. The updated search results are provided to a user. Updating the search results includes determining a ranking (or a re-ranking) for each item of content included in the search results based on the first search constraint, the second search constraint, and the weight value. Re-ranking the search results may further be based on a weight value associated with the first search constraint, such as a default or maximum weight value.

    Automatically curated image searching
    10.
    发明授权
    Automatically curated image searching 有权

    公开(公告)号：US11361018B2

    公开(公告)日：2022-06-14

    申请号：US15824907

    申请日：2017-11-28

    Applicant: ADOBE INC.

    Inventor： Samarth Gulati ,  Brett Michael Butterfield ,  Baldo Faieta ,  Kent Andrew Edmonds

    IPC: G06F16/532 ,  G06F16/54 ,  G06F16/56 ,  G06F16/583 ,  G06F16/9535

    Abstract: Systems and methods for searching digital content are disclosed. A method includes receiving, from a user, a base search constraint. A search constraint includes search values or criteria. A recall set is generated based on the base search constraint. Recommended search constraints are determined and provided to the user. The recommended search constraints are statistically associated with the base search constraint. The method receives, from the user, a selection of a first search constraint included in the plurality of recommend search constraints. The method generates and provides search results to the user that include a re-ordering of the recall set. The re-ordering is based on a search constraint set that includes both the base search constraint and the selected first search constraint. The re-ordering is further based on a weight associated with the base search constraint and another user-provided weight associated with the first search constraint.

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