DocumentCode :
3605450
Title :
Rating Image Aesthetics Using Deep Learning
Author :
Xin Lu ; Zhe Lin ; Hailin Jin ; Jianchao Yang ; Wang, James Z.
Author_Institution :
Coll. of Inf. Sci. & Technol., Pennsylvania State Univ., University Park, PA, USA
Volume :
17
Issue :
11
fYear :
2015
Firstpage :
2021
Lastpage :
2034
Abstract :
This paper investigates unified feature learning and classifier training approaches for image aesthetics assessment . Existing methods built upon handcrafted or generic image features and developed machine learning and statistical modeling techniques utilizing training examples. We adopt a novel deep neural network approach to allow unified feature learning and classifier training to estimate image aesthetics. In particular, we develop a double-column deep convolutional neural network to support heterogeneous inputs, i.e., global and local views, in order to capture both global and local characteristics of images . In addition, we employ the style and semantic attributes of images to further boost the aesthetics categorization performance . Experimental results show that our approach produces significantly better results than the earlier reported results on the AVA dataset for both the generic image aesthetics and content -based image aesthetics. Moreover, we introduce a 1.5-million image dataset (IAD) for image aesthetics assessment and we further boost the performance on the AVA test set by training the proposed deep neural networks on the IAD dataset.
Keywords :
convolution; image classification; learning (artificial intelligence); neural nets; AVA dataset; IAD; classifier training approaches; content -based image aesthetics; double-column deep convolutional neural network; generic image aesthetics; image aesthetics assessment; machine learning; novel deep neural network approach; statistical modeling techniques; unified feature learning; Computer architecture; Image color analysis; Machine learning; Neural networks; Semantics; Training; Visualization; Automatic feature learning; deep neural networks; image aesthetics;
fLanguage :
English
Journal_Title :
Multimedia, IEEE Transactions on
Publisher :
ieee
ISSN :
1520-9210
Type :
jour
DOI :
10.1109/TMM.2015.2477040
Filename :
7243357
Link To Document :
بازگشت