Composition and Style Attributes Guided Image Aesthetic Assessment
- URL: http://arxiv.org/abs/2111.04647v1
- Date: Mon, 8 Nov 2021 17:16:38 GMT
- Title: Composition and Style Attributes Guided Image Aesthetic Assessment
- Authors: Luigi Celona and Marco Leonardi and Paolo Napoletano and Alessandro
Rozza
- Abstract summary: We propose a method for the automatic prediction of the aesthetics of an image.
The proposed network includes: a pre-trained network for semantic features extraction (the Backbone); a Multi Layer Perceptron (MLP) network that relies on the Backbone features for the prediction of image attributes (the AttributeNet)
Given an image, the proposed multi-network is able to predict: style and composition attributes, and aesthetic score distribution.
- Score: 66.60253358722538
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The aesthetic quality of an image is defined as the measure or appreciation
of the beauty of an image. Aesthetics is inherently a subjective property but
there are certain factors that influence it such as, the semantic content of
the image, the attributes describing the artistic aspect, the photographic
setup used for the shot, etc. In this paper we propose a method for the
automatic prediction of the aesthetics of an image that is based on the
analysis of the semantic content, the artistic style and the composition of the
image. The proposed network includes: a pre-trained network for semantic
features extraction (the Backbone); a Multi Layer Perceptron (MLP) network that
relies on the Backbone features for the prediction of image attributes (the
AttributeNet); a self-adaptive Hypernetwork that exploits the attributes prior
encoded into the embedding generated by the AttributeNet to predict the
parameters of the target network dedicated to aesthetic estimation (the
AestheticNet). Given an image, the proposed multi-network is able to predict:
style and composition attributes, and aesthetic score distribution. Results on
three benchmark datasets demonstrate the effectiveness of the proposed method,
while the ablation study gives a better understanding of the proposed network.
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