CASIA OpenIR  > 智能感知与计算研究中心
Automatic Image Cropping with Aesthetic Map and Gradient Energy Map
Yueying Kao1,2; Ran He(赫然)1,2,3; Kaiqi Huang1,2,3; Huang, Kaiqi
2017-03
Conference NameIEEE International Conference on Acoustics, Speech, and Signal Processing
Source PublicationIEEE International Conference on Acoustics, Speech, and Signal Processing 2017
Conference Date2017-3-5
Conference PlaceNEW ORLEANS, USA
Abstract
Image cropping is a fundamental task in image editing to
enhance the aesthetic quality of images. In this paper,
we propose an automatic image cropping technique based
on aesthetic map and gradient energy map. Instead of
utilizing aesthetic rules in previous methods, we learn the
aesthetic map by a deep convolutional neural network with
a large-scale dataset for aesthetic quality assessment. The
aesthetic map can highlight the discriminative image regions
for high (or low) aesthetic quality category. The gradient
energy map presents edge spatial distribution of images
and is developed to compute the simplicity of images.
Then a composition model is learned with the aesthetic
map and gradient energy map to evaluate the quality of
composition for crops. Moreover, an aesthetic preservation
model is developed to compute the aesthetic information
remained in crops to avoid cropping out high aesthetic
regions. Experiments show that our approach significantly
outperforms state-of-the-art cropping methods.
KeywordImage Cropping Aesthetic Map Gradient Energy Map Convolutional Neural Networks
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14660
Collection智能感知与计算研究中心
Corresponding AuthorHuang, Kaiqi
Affiliation1.CRIPAC & NLPR, CASIA
2.University of Chinese Academy of Sciences
3.CAS Center for Excellence in Brain Science and Intelligence Technology
Recommended Citation
GB/T 7714
Yueying Kao,Ran He,Kaiqi Huang,et al. Automatic Image Cropping with Aesthetic Map and Gradient Energy Map[C],2017.
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