Rating Image Aesthetics Using Deep Learning - Versionsgeschichte

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== Reference ==
Lu, X.; Lin, Z.; Jin, H.; Yang, J.; Wang, J.Z.: [[Rating Image Aesthetics Using Deep Learning]]. IEEE Transactions on Multimedia, 2015, Volume: 17, Issue: 11, 2021 - 2034.

== DOI ==
http://dx.doi.org/10.1109/TMM.2015.2477040

== 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.

== Extended Abstract ==

== Bibtex ==
@ARTICLE{7243357,
author={Lu, X. and Lin, Z. and Jin, H. and Yang, J. and Wang, J.Z.},
journal={Multimedia, IEEE Transactions on},
title={Rating Image Aesthetics Using Deep Learning},
year={2015},
volume={17},
number={11},
pages={2021-2034},
keywords={Computer architecture;Image color analysis;Machine learning;Neural networks;Semantics;Training;Visualization;Automatic feature learning;deep neural networks;image aesthetics},
doi={10.1109/TMM.2015.2477040},
url={http://dx.doi.org/10.1109/TMM.2015.2477040, http://de.evo-art.org/index.php?title=Rating_Image_Aesthetics_Using_Deep_Learning },
ISSN={1520-9210},
month={Nov},
}

== Used References ==
References are not available for this document.

== Links ==
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 J. Z. Wang, “On shape and the computability of emotions,” in ACM
 International Conference on Multimedia (MM), pp. 229–238, 2012 http://dx.doi.org/10.1145/2393347.2393384
 == Links ==

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 == Used References ==
-References are not available for this document.
+R. Datta, D. Joshi, J. Li and J. Wang, "Studying aesthetics in photographic images using a computational approach", Proc. Eur. Conf. Comput. Vis., pp. 288-301
 == Links ==
 === Full Text ===
+http://infolab.stanford.edu/~wangz/project/imsearch/Aesthetics/TMM15/lu.pdf
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