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Photo Aesthetics Analysis via DCNN Feature Encoding

Title
Photo Aesthetics Analysis via DCNN Feature Encoding
Authors
Lee, Hui-JinHong, Ki-SangKang, HenryLee, Seungyong
POSTECH Authors
Hong, Ki-SangLee, Seungyong
Date Issued
Mar-2017
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Abstract
We propose an automatic framework for quality assessment of a photograph as well as analysis of its aesthetic attributes. In contrast to the previous methods that rely on manually designed features to account for photo aesthetics, our method automatically extracts such features using a pretrained deep convolutional neural network (DCNN). To make the DCNN-extracted features more suited to our target tasks of photo quality assessment and aesthetic attribute analysis, we propose a novel feature encoding scheme, which supports vector machines-driven sparse restricted Boltzmann machines, which enhances sparseness of features and discrimination between target classes. Experimental results show that our method outperforms the current state-of-the-art methods in automatic photo quality assessment, and gives aesthetic attribute ratings that can be used for photo editing. We demonstrate that our feature encoding scheme can also be applied to general object classification task to achieve performance gains.
Keywords
QUALITY ASSESSMENT; CLASSIFICATION
URI
http://oasis.postech.ac.kr/handle/2014.oak/50824
DOI
10.1109/TMM.2017.2687759
ISSN
1520-9210
Article Type
Article
Citation
IEEE TRANSACTIONS ON MULTIMEDIA, vol. 19, no. 8, page. 1921 - 1932, 2017-03
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 HONG, KI SANG
Dept of Electrical Enginrg
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