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2016 | OriginalPaper | Buchkapitel

Predicting Image Aesthetics with Deep Learning

verfasst von : Simone Bianco, Luigi Celona, Paolo Napoletano, Raimondo Schettini

Erschienen in: Advanced Concepts for Intelligent Vision Systems

Verlag: Springer International Publishing

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Abstract

In this paper we investigate the use of a deep Convolutional Neural Network (CNN) to predict image aesthetics. To this end we fine-tune a canonical CNN architecture, originally trained to classify objects and scenes, by casting the image aesthetic prediction as a regression problem. We also investigate whether image aesthetic is a global or local attribute, and the role played by bottom-up and top-down salient regions to the prediction of the global image aesthetic. Experimental results on the canonical Aesthetic Visual Analysis (AVA) dataset show the robustness of the solution proposed, which outperforms the best solution in the state of the art by almost 17 % in terms of Mean Residual Sum of Squares Error (MRSSE).

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Titel: Predicting Image Aesthetics with Deep Learning
verfasst von: Simone Bianco
Luigi Celona
Paolo Napoletano
Raimondo Schettini
Verlag: Springer International Publishing
Buch: Advanced Concepts for Intelligent Vision Systems
Print ISBN: 978-3-319-48679-6

Electronic ISBN: 978-3-319-48680-2

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-48680-2_11

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