Utilizing interactive deep learning to select objects in digital visual media
AssigneeAdobe Systems Incorporated
InventorsBrian Price, Scott Cohen, Ning Xu
Life of this patent
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Abstract
Systems and methods are disclosed for selecting target objects within digital images. In particular, in one or more embodiments, the disclosed systems and methods generate a trained neural network based on training digital images and training indicators. Moreover, one or more embodiments of the disclosed systems and methods utilize a trained neural network and iterative user indicators to select targeted objects in digital images. Specifically, the disclosed systems and methods can transform user indicators into distance maps that can be utilized in conjunction with color channels and a trained neural network to identify pixels that reflect the target object.
Claims20
In a digital medium environment for editing digital visual media, a method of interactively selecting digital objects represented within digital visual media using deep learning, the method comprising:
- accessing a neural network trained utilizing a repository of digital training images having target objects, training ground truth masks of the target objects, training indicators, and training distance maps, each training distance map reflecting distances between a corresponding training indicator and pixels of a corresponding digital training image;
- identifying a user indicator with regard to a probe digital image, the user indicator comprising one or more pixels of the probe digital image identified by a user and an indication of how the one or more pixels correspond to a target object represented in the probe digital image;
- generating a distance map reflecting distances between the user indicator and pixels of the probe digital image;
- and
- identifying a set of pixels representing the target object in the probe digital image by providing the probe digital image, the user indicator, and the generated distance map as input to the trained neural network.
A non-transitory computer readable medium storing instructions that, when executed by at least one processor, cause a computer system to:
- access a neural network trained utilizing digital training images having target objects, training ground truth masks of the target objects, training indicators, and training distance maps, each training distance map reflecting distances between a corresponding training indicator and pixels of a corresponding digital training image;
- identify a user indicator with regard to a probe digital image, the user indicator comprising one or more pixels of the probe digital image identified by a user and an indication of how the one or more pixels correspond to a target object represented in the probe digital image;
- generate a distance map reflecting distances between the user indicator and pixels of the probe digital image;
- and
- identify a set of pixels representing the target object in the probe digital image by providing the probe digital image, the user indicator, and the generated distance map as input to the trained neural network.
A system comprising:
- at least one processor;
- and
- at least one non-transitory computer readable storage medium storing instructions that, when executed by the at least one processor, cause the system to:
- access a neural network trained utilizing a repository of digital training images having target objects, training ground truth masks of the target objects, training indicators, and training distance maps, each training distance map reflecting distances between a corresponding training indicator and pixels of a corresponding digital training image;
- identify a user indicator with regard to a probe digital image, the user indicator comprising one or more pixels of the probe digital image identified by a user and an indication of how the one or more pixels correspond to a target object represented in the probe digital image;
- generate a distance map reflecting distances between the user indicator and pixels of the probe digital image;
- and
- identify a set of pixels representing the target object in the probe digital image by providing the probe digital image, the user indicator, and the generated distance map as input to the trained neural network.
Dependencies are inferred from the claim text.