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    IDENTIFYING UNKNOWN PERSON INSTANCES IN IMAGES
    41.
    发明申请
    IDENTIFYING UNKNOWN PERSON INSTANCES IN IMAGES 审中-公开

    公开(公告)号：US20180336401A1

    公开(公告)日：2018-11-22

    申请号：US16049322

    申请日：2018-07-30

    Applicant: Adobe Systems Incorporated

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

    IPC: G06K9/00 ,  G06K9/66 ,  G06K9/62

    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.

    Automatically segmenting images based on natural language phrases
    42.
    发明授权
    Automatically segmenting images based on natural language phrases 有权

    公开(公告)号：US10089742B1

    公开(公告)日：2018-10-02

    申请号：US15458887

    申请日：2017-03-14

    Applicant: ADOBE SYSTEMS INCORPORATED

    Inventor： Zhe Lin ,  Xin Lu ,  Xiaohui Shen ,  Jimei Yang ,  Chenxi Liu

    IPC: G06K9/00 ,  G06T7/11 ,  G06F17/27 ,  G10L15/26

    Abstract: The invention is directed towards segmenting images based on natural language phrases. An image and an n-gram, including a sequence of tokens, are received. An encoding of image features and a sequence of token vectors are generated. A fully convolutional neural network identifies and encodes the image features. A word embedding model generates the token vectors. A recurrent neural network (RNN) iteratively updates a segmentation map based on combinations of the image feature encoding and the token vectors. The segmentation map identifies which pixels are included in an image region referenced by the n-gram. A segmented image is generated based on the segmentation map. The RNN may be a convolutional multimodal RNN. A separate RNN, such as a long short-term memory network, may iteratively update an encoding of semantic features based on the order of tokens. The first RNN may update the segmentation map based on the semantic feature encoding.

    Combined composition and change-based models for image cropping
    44.
    发明授权
    Combined composition and change-based models for image cropping 有权

    公开(公告)号：US10019823B2

    公开(公告)日：2018-07-10

    申请号：US14062751

    申请日：2013-10-24

    Applicant: Adobe Systems Incorporated

    Inventor： Zhe Lin ,  Radomir Mech ,  Peng Wang

    IPC: G09G5/00 ,  G06T11/60 ,  G06T3/00

    CPC classification number: G06T11/60 ,  G06N7/005 ,  G06N20/10 ,  G06T3/0012 ,  G06T2207/20132

    Abstract: In techniques of combined composition and change-based models for image cropping, a composition application is implemented to apply one or more image composition modules of a learned composition model to evaluate multiple composition regions of an image. The learned composition model can determine one or more cropped images from the image based on the applied image composition modules, and evaluate a composition of the cropped images and a validity of change from the image to the cropped images. The image composition modules of the learned composition model include a salient regions module that iteratively determines salient image regions of the image, and include a foreground detection module that determines foreground regions of the image. The image composition modules also include one or more imaging models that reduce a number of the composition regions of the image to facilitate determining the cropped images from the image.

    Imaging process initialization techniques
    46.
    发明授权
    Imaging process initialization techniques 有权

    公开(公告)号：US09911201B2

    公开(公告)日：2018-03-06

    申请号：US15191141

    申请日：2016-06-23

    Applicant: Adobe Systems Incorporated

    Inventor： Xin Lu ,  Zhe Lin

    IPC: G06T7/40 ,  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.

    Convolutional Neural Network Joint Training
    47.
    发明申请
    Convolutional Neural Network Joint Training 审中-公开

    公开(公告)号：US20170357892A1

    公开(公告)日：2017-12-14

    申请号：US15177121

    申请日：2016-06-08

    Applicant: Adobe Systems Incorporated

    Inventor： Zhe Lin ,  Yufei Wang ,  Radomir Mech ,  Xiaohui Shen ,  Gavin Stuart Peter Miller

    IPC: G06N3/08 ,  G06N3/04

    Abstract: In embodiments of convolutional neural network joint training, a computing system memory maintains different data batches of multiple digital image items, where the digital image items of the different data batches have some common features. A convolutional neural network (CNN) receives input of the digital image items of the different data batches, and classifier layers of the CNN are trained to recognize the common features in the digital image items of the different data batches. The recognized common features are input to fully-connected layers of the CNN that distinguish between the recognized common features of the digital image items of the different data batches. A scoring difference is determined between item pairs of the digital image items in a particular one of the different data batches. A piecewise ranking loss algorithm maintains the scoring difference between the item pairs, and the scoring difference is used to train CNN regression functions.

    UTILIZING DEEP LEARNING FOR RATING AESTHETICS OF DIGITAL IMAGES
    48.
    发明申请
    UTILIZING DEEP LEARNING FOR RATING AESTHETICS OF DIGITAL IMAGES 有权

    公开(公告)号：US20170294010A1

    公开(公告)日：2017-10-12

    申请号：US15097113

    申请日：2016-04-12

    Applicant: Adobe Systems Incorporated

    Inventor： Xiaohui Shen ,  Zhe Lin ,  Shu Kong ,  Radomir Mech

    IPC: G06T7/00 ,  G06K9/62

    CPC classification number: G06T7/0002 ,  G06K9/623 ,  G06K9/6256 ,  G06T2207/20081 ,  G06T2207/30168

    Abstract: Systems and methods are disclosed for estimating aesthetic quality of digital images using deep learning. In particular, the disclosed systems and methods describe training a neural network to generate an aesthetic quality score digital images. In particular, the neural network includes a training structure that compares relative rankings of pairs of training images to accurately predict a relative ranking of a digital image. Additionally, in training the neural network, an image rating system can utilize content-aware and user-aware sampling techniques to identify pairs of training images that have similar content and/or that have been rated by the same or different users. Using content-aware and user-aware sampling techniques, the neural network can be trained to accurately predict aesthetic quality ratings that reflect subjective opinions of most users as well as provide aesthetic scores for digital images that represent the wide spectrum of aesthetic preferences of various users.

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