ILGNet: inception modules with connected local and global features for efficient image aesthetic quality classification using domain adaptation

ILGNet: inception modules with connected local and global features for efficient image aesthetic quality classification using domain adaptation

In this study, the authors address a challenging problem of aesthetic image classification, which is to label an input image as high‐ or low‐aesthetic quality. We take both the local and global features of images into consideration. A novel deep convolutional neural network named ILGNet is proposed,...

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Bibliographic Details
Main Authors: Xin Jin, Le Wu, Xiaodong Li, Xiaokun Zhang, Jingying Chi, Siwei Peng, Shiming Ge, Geng Zhao, Shuying Li
Format: Article
Language:English
Published: Wiley 2019-03-01
Series:IET Computer Vision
Subjects:
ILGNet
inception modules
connected local features
connected global features
efficient image aesthetic quality classification
domain adaptation
Online Access:https://doi.org/10.1049/iet-cvi.2018.5249

Internet

https://doi.org/10.1049/iet-cvi.2018.5249

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