Machine learning models for exposure defect classification in images
AssigneeAdobe Inc.
InventorsAkhilesh KUMAR, Zhe Lin, William Lawrence Marino
Life of this patent
Estimating the term…
Abstract
Models for classifying exposure defects in images are provided by training a binary model on a dataset of images labeled to indicate exposure within the images. When trained, the binary model classifies an image based on whether the image includes an exposure defect. A classification model is also trained. The classification model is trained on a dataset of images having exposure defects labeled to indicate exposure scores or exposure defect classifications. When trained, the classification model classifies the image based on a level of exposure. The binary model and the classification model can be stored for identifying and classifying exposure defects within images.
Claims20
A method comprising:
- training a binary model at least in part on a dataset of images labeled to indicate exposure within the images, such that the binary model, when trained, classifies an image based on whether the image includes an exposure defect;
- training a classification model at least in part on a dataset of images having exposure defects labeled to indicate exposure scores or exposure defect classifications, such that the classification model, when trained, classifies the image based on a level of exposure;
- and
- storing the binary model and the classification model for identifying and classifying exposure defects.
One or more computer storage media storing computer-useable instructions that, when executed by one or more computing devices, cause the one or more computing devices to perform operations comprising:
- training a binary model at least in part on a dataset of images labeled to indicate exposure within the images, such that the binary model, when trained, classifies an image based on whether the image includes an exposure defect;
- training at least two classification models at least in part on a dataset of images having exposure defects labeled to indicate exposure scores or exposure defect classifications, such that a first classification model, when trained, is configured to classify the image based on a level of underexposure, and such that a second classification model, when trained, is configured to classify the image based on a level of overexposure;
- and
- storing the binary model and the at least two classification models for identifying and classifying exposure defects.
A system comprising one or more processors and memory configured to provide computer program instructions to the one or more processors, the computer program instructions causing the one or more processors to:
- train a first neural network binary model at least in part on a dataset of images labeled to indicate exposure within the images, such that the first neural network binary model, when trained, classifies an image based on whether the image includes an exposure defect;
- train a second neural network classification model at least in part on a dataset of images having exposure defects labeled to indicate exposure scores or exposure defect classifications, such that the second neural network classification model, when trained, classifies the image from the first neural network binary model based on a level of exposure;
- and
- store the first neural network binary model and the second neural network classification model for identifying and classifying exposure defects.
Dependencies are inferred from the claim text.