Brain-Inspired Deep Networks for Image Aesthetics Assessment
Inhaltsverzeichnis
Referenz
Zhangyang Wang, Shiyu Chang, Florin Dolcos, Diane Beck, Ding Liu, and Thomas Huang: Brain-Inspired Deep Networks for Image Aesthetics Assessment. arXiv:1601.04155v2 [cs.CV] 15 Mar 2016
DOI
Abstract
Image aesthetics assessment has been challenging due to its subjective nature. Inspired by the scientific advances in the human visual perception and neuroaesthetics, we design Brain-Inspired Deep Networks (BDN) for this task. BDN first learns attributes through the parallel supervised pathways, on a variety of selected feature dimensions. A high-level synthesis network is trained to associate and transform those attributes into the overall aesthetics rating. We then extend BDN to predicting the distribution of human ratings, since aesthetics ratings are often subjective. Another highlight is our first-of-its-kind study of label-preserving transformations in the context of aesthetics assessment, which leads to an effective data augmentation approach. Experimental results on the AVA dataset show that our biological inspired and task-specific BDN model gains significantly performance improvement, compared to other state-of-the-art models with the same or higher parameter capacity.
Extended Abstract
Bibtex
@article{DBLP:journals/corr/WangDBCH16,
author = {Zhangyang Wang andFlorin Dolcos and Diane Beck and Shiyu Chang and Thomas S. Huang},
title = {Brain-Inspired Deep Networks for Image Aesthetics Assessment},
journal = {CoRR},
volume = {abs/1601.04155},
year = {2016},
url = {http://arxiv.org/abs/1601.04155 http://de.evo-art.org/index.php?title=Brain-Inspired_Deep_Networks_for_Image_Aesthetics_Assessment},
timestamp = {Mon, 01 Feb 2016 15:36:05 +0100},
biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/WangDBCH16},
bibsource = {dblp computer science bibliography, http://dblp.org}
}
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Links
Full Text
https://arxiv.org/pdf/1601.04155
