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Savoias-Dataset

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A visual complexity dataset across seven different categories, including Scenes, Advertisements, Visualization and infographics, Objects, Interior design, Art, and Suprematism for computer vision application.

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# SAVOIAS Dataset

we introduce **SAVOIAS** a visual complexity dataset that compromises of more than 1,400 images from seven image categories
namely [**Scenes**](http://places2.csail.mit.edu/), [**Advertisements**](http://people.cs.pitt.edu/~kovashka/ads/), [**Visualization and infographics**](http://massvis.mit.edu/), [**Objects**](http://cocodataset.org/#home), **Interior design**,
[**Art**](https://github.com/BathVisArtData/PeopleArt), and [**Suprematism**](https://github.com/BathVisArtData/PeopleArt/tree/master/JPEGImages/Suprematism). The images in each category portray diverse characteristics including various low-level and high-level features, objects, backgrounds, textures and patterns, text, and graphics. The ground truth for SAVOIAS is obtained by crowdsourcing more than 37,000 pairwise comparisons of images using the forced-choice methodology and with more than 1,600
contributors using the [Figure-Eight](http://figure-eight.com/) crowdplatform. The resulting relative scores are then converted to absolute visual complexity scores using the Bradley-Terry
method and matrix completion. When applying five state-of-the-art algorithms to analyze the visual complexity of the images
in the SAVOIAS dataset, we found that the scores obtained from these baseline tools only correlate well with crowd-sourced
labels for abstract patterns in the Suprematism category (Pearson correlation r=0.84). For the other categories, 
in particular, the objects and advertisement categories, low correlation coefficients were revealed (r=0.3 and 0.56,
respectively). These findings suggest that (1) state-of-the-art approaches are mostly insufficient and (2) SAVOIAS enables
category-specific method development, which is likely to improve the impact of visual complexity analysis on specific
application areas, including computer vision.

## Usage

git clone the repositoty:
```
git clone https://github.com/esaraee/Savoias-Dataset.git
```
1420 images of the SAVOIAS dataset are located in the respective 7 categories the belong to in **Images** folder. In addition, if researchers are interested to find the original name of the images they can use the Name_Mapping folder to retreive those. The visual complexity ground-truth scores for the SAVOIAS images is located in **Ground truth** folder.

### Baseline Comparison for SAVOIAS Dataset

![alt text](https://raw.githubusercontent.com/esaraee/Savoias-Dataset/master/baselines.png)

### Image Samples from SAVOIAS Dataset

![alt text](https://raw.githubusercontent.com/esaraee/Savoias-Dataset/master/SAVOIAS-image-samples.png)

### Results of Deep Learning-based Visual Complexity Prediction on SAVOIAS Dataset

Sample images of the Savoias dataset and their corresponding energy maps from the fourth max-pooling layer in the VGG-16 architecture trained for the scene recognitiontask. The images from top left to bottom right belong to datasets Advertisement, Places2, MASSVIS (Visualization and Infographics), MSCOCO, IKEA, and art respectively. Notethat the last three images belong to the art dataset where the last sample is from the Suprematism category.

![alt text](https://raw.githubusercontent.com/esaraee/Savoias-Dataset/master/fourth_max_pooling.png)

## Getting help

If you have any question, concern, or bug report, please file an issue in this repository's Issue Tracker and we will respond accordingly.

## Funding
This research was partially funded by the following NSF Awards:

- [NSF Award #1421943](https://nsf.gov/awardsearch/showAward?AWD_ID=1421943) RI: Small: Using Humans in the Loop to Collect High-quality Annotations from Images and Time-lapse Videos of Cells
- [NSF Award #1838193](https://www.nsf.gov/awardsearch/showAward?AWD_ID=1838193&HistoricalAwards=false) BIGDATA: IA: Multiplatform, Multilingual, and Multimodal Tools for Analyzing Public Communication in over 100 Languages

## Acknowledgement

We would like to thank the students, [Yifu Hu](http://cs-people.bu.edu/yfhu) and [Yi Zheng](http://cs-people.bu.edu/yizheng/) who prepared the images for the interior design category of our dataset.

## Citation 
Please cite the following papers in your publications if it helps you with your research.
```
@article{SaraeeJaBe18,
  title={SAVOIAS: A Diverse, Multi-Category Visual Complexity Dataset},
  author={Saraee, Elham and Jalal, Mona and Betke, Margrit},
  journal={arXiv preprint arXiv:1810.01771},
  year={2018}
}

@article{SaraeeJaBe20,
  title={Visual complexity analysis using deep intermediate-layer features},
  author={Saraee, Elham and Jalal, Mona and Betke, Margrit},
  journal={Computer Vision and Image Understanding},
  pages={102949},
  year={2020},
  publisher={Elsevier}
}

```

You can download the PDF of SAVAOIAS paper [here](http://monajalal.github.io/assets/pdf/SAVOIAS_Saraee_arXiv.pdf) and the PDF of our paper that uses deep learning on SAVOIAS dataset to predict the visual complexity [here](http://monajalal.github.io/assets/pdf/CVIU2020_Saraee.pdf).

## License

SAVOIAS dataset is freely and publicly available under [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).
For further information regarding the copyright of the images used in the Savioas dataset, please check the license file.

## Story Behind Naming
Savoia is derived from [Margherita di Savoia](https://en.wikipedia.org/wiki/Margherita_di_Savoia,_Apulia), a town in Italy. Here is a beautiful picture of Salt crystal in salt brine, salt production at Margherita di Savoia, Apulia, Italy by Franz Aberham via Getty Images. 
![alt text](https://raw.githubusercontent.com/esaraee/Savoias-Dataset/master/savoia.jpg)

## Credits

[Elham Saraee](http://cs-people.bu.edu/esaraee/), [Mona Jalal](http://monajalal.com), [Margrit Betke](http://www.cs.bu.edu/~betke/)

Savoias-Dataset

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