Justin900429 / computational-aesthetics

Python version for extracting computational aesthetics features

README

Introduction

This repo is to extract the computational aesthetic features introduced from the paper - "The Pictures we Like are our Image: Continuous Mapping of Favorite Pictures into Self-Assessed and Attributed Personality Traits". Some of the features had been removed and readapted. See the table below.

Feature Synopis

Category	Name	dimension
Color	HSV statics Emotion-based Color diversity Color name	5 3 1 11
Composition	Edge pixels Level of detial Average region size Low depth of field (DOF) Rule of thirds Image size	1 1 1 3 2 1
Textural Properties	Gray distribution entropy Wavelet based textures Tamura GLCM - features	1 12 3 3

The description column is omitted. To see what each feature does, please refer to the paper. Additionally, there are some changes being made.

1. The Faces features was removed.
2. The realization of color diversity is different from the paper.
3. The GIST descriptors was removed.
4. The GLCM - features used only gray image and left only 4 features.

Installation

Install requirements

$ pip install git+https://github.com/Justin900429/computational-aesthetics

Import file

from CA import CA
...
# Create objects
img_path = "..."
ca = CA(img_path)
res = ca.compute_ca()
...
# update image path
new_path = "..."
ca.update(new_path)
new_res = ca.compute_ca()
...

See example for more details

Citation

@ARTICLE{
  7378902,
  author={Segalin, Crisitina and Perina, Alessandro and Cristani, Marco and Vinciarelli, Alessandro},
  journal={IEEE Transactions on Affective Computing},
  title={The Pictures We Like Are Our Image: Continuous Mapping of Favorite Pictures into Self-Assessed and Attributed Personality Traits},
  year={2017},
  volume={8},
  number={2},
  pages={268-285},
  doi={10.1109/TAFFC.2016.2516994}
}