期刊
2019 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
卷 -, 期 -, 页码 3771-3775出版社
IEEE
DOI: 10.1109/icip.2019.8803599
关键词
Image Aesthetics Assessment; Low-rank Bilinear Pooling; Hierarchical Features
资金
- National Natural Science Foundation of China [61527804, 61831015, 61701351]
Image aesthetics assessment is an interesting yet challenging topic which can be applied on numerous scenarios such as high quality image retrieval or recommendation systems. We propose a hierarchical features fusion aesthetic assessment (HFFAA) model for this task. HFFAA is a two-stream convolutional neural network (CNN) which is composed of two branches with heterogeneous and complementary aesthetic perceptual abilities. HFFAA learns the mapping from deep image representation into their ground-truth aesthetic labels (good or bad) in an end-to-end fashion. Extensive experiments demonstrate that the proposed model achieves superior performance on two widely evaluated public benchmark databases, i.e., CUHKPQ and AVA. We also validate the rationality of the designs of HFFAA through a series of ablation experiments.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据