https://github.com/justin900429/computational-aesthetics

Python version for extracting computational aesthetics features
https://github.com/justin900429/computational-aesthetics

computational-aesthetics image-feature-extraction paper-implementations python python-package

Last synced: 28 days ago
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Python version for extracting computational aesthetics features

• Host: GitHub
• URL: https://github.com/justin900429/computational-aesthetics
• Owner: Justin900429
• License: mit
• Created: 2021-08-28T09:24:27.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2022-10-28T06:06:04.000Z (about 2 years ago)
• Last Synced: 2023-03-05T03:24:30.936Z (almost 2 years ago)
• Topics: computational-aesthetics, image-feature-extraction, paper-implementations, python, python-package
• Language: Python
• Homepage:
• Size: 19.5 KB
• Stars: 5
• Watchers: 1
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# 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"](https://ieeexplore.ieee.org/document/7378902). 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

```python

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](https://github.com/Justin900429/computational-aesthetics/blob/main/example.py) for more details

## Citation

```bibtex

@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}

}

```