公开(公告)号：US20200065956A1

公开(公告)日：2020-02-27

申请号：US16670314

申请日：2019-10-31

Applicant: Adobe Inc.

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

IPC: G06T7/00 ,  G06K9/62 ,  G06N3/08 ,  G06N3/04

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.

公开(公告)号：US10515443B2

公开(公告)日：2019-12-24

申请号：US15981166

申请日：2018-05-16

Applicant: Adobe Inc.

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

IPC: G06K9/62 ,  G06T7/00

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.