Photography style analysis using Convolutional Neural Networks
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Keywords
Photography ; Image aesthetics ; Deep learning ; CNN ; ResNet50Abstract
This thesis studies the artistic nature of photography and tries to construct a
framework for the definition of the term “photography style”. It goes deep
into the history of photography and analyzes a plethora of aesthetics that have been
carved throughout the ages. Through this journey it collects the most important
rules of aesthetics and groups them in specific categories. Then, with the help of
deep learning and computer vision, it is able to train and predict on those specific
categories.
Firstly, the reader is introduced to the world of photography. We present its
historical background and then focus on its huge rise in the era of the social media.
We then analyze some basics of photography, as well as some of the most known
rules of aesthetics. We finally emphasize on the difficulty to bind those rules into a
specific problem with specific tasks due to the subjectivity of photography and arts
in general.
We then present a novel dataset of photographs annotated in terms of the respective
image aesthetics. We also examine the ability of Convolutional Neural Networks
(CNNs) to distinguish between the adopted photography style classes. In particular,
we have defined five photography style classification tasks, related to the following
aesthetic attributes: Color, Depth of Field (DoF), Palette, Composition and Type.
We then followed an annotation procedure using on a set of 1832 photos selected
from the Unsplash Full dataset. Multiple annotators have also been used, in order
to measure inter-annotator agreement.
As soon as the dataset was compiled, we trained and evaluated a Residual Neural
Network (ResNet50). The experimental results prove that, despite the imbalanced
dataset, our model was able to achieve acceptable classification results. The dataset
is openly provided, along with the trained models and Python code to use them.