Utilizing deep learning for rating aesthetics of digital images
First Claim
1. A computer-implemented method of estimating aesthetic quality of digital images using deep learning, the method comprising:
- receiving a plurality of training images with associated user provided ratings;
sampling the plurality of training images to identify pairs of training images; and
training a neural network to output aesthetic quality scores for identified pairs of training images that, for a given pair of training images, minimizes a difference between predicted user ratings and average user ratings of the user provided ratings for the respective training images in the given pair of training images while maintaining a relative difference between the associated user provided ratings of the training images in the given pair of training images.
2 Assignments
0 Petitions
Accused Products
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.
29 Citations
20 Claims
-
1. A computer-implemented method of estimating aesthetic quality of digital images using deep learning, the method comprising:
-
receiving a plurality of training images with associated user provided ratings; sampling the plurality of training images to identify pairs of training images; and training a neural network to output aesthetic quality scores for identified pairs of training images that, for a given pair of training images, minimizes a difference between predicted user ratings and average user ratings of the user provided ratings for the respective training images in the given pair of training images while maintaining a relative difference between the associated user provided ratings of the training images in the given pair of training images. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
-
-
13. A non-transitory computer readable storage medium storing instructions thereon that, when executed by at least one processor, cause a computer system to:
-
receive a digital image; and generate an aesthetic quality score for the digital image and an attribute quality score for each of a plurality of attributes of the digital image using a neural network having a training structure that jointly learns low level parameters for pairs of training images of a plurality of training images and includes an attribute model for each of the plurality of attributes that utilizes the jointly learned low level parameters and outputs an attribute quality score for a given attribute. - View Dependent Claims (14, 15, 16, 17, 18)
-
-
19. A system for analyzing digital images to estimate aesthetic quality of the digital images using deep learning, the system comprising:
-
at least one processor; a non-transitory storage medium comprising instructions that, when executed by the at least one processor, cause the system to; receive a plurality of training images with user provided ratings; sample the plurality of training images to identify pairs of images that are rated by one or more common users, pairs of images having a common type of content, or pairs of images that are rated by different users; and train a neural network to output aesthetic quality scores for identified pairs of training images that, for a given pair of training images, minimizes a difference between predicted user ratings and average user ratings of associated user provided ratings for the respective training images in the given pair of training images while maintaining a relative difference between the associated user provided ratings of the given pair of training images. - View Dependent Claims (20)
-
Specification