paper added figure

paper/paper.bib

@@ -74,4 +74,12 @@ @Thesis{Gabrieli2021
     type        = {phdthesis}, % mathesis and phdthesis work here
     institution = {Nanyang Technological University},
     year        = {2021},
-}
+}
+
+@inproceedings{miniukovich2014quantification,
+  title={Quantification of interface visual complexity},
+  author={Miniukovich, Aliaksei and De Angeli, Antonella},
+  booktitle={Proceedings of the 2014 international working conference on advanced visual interfaces},
+  pages={153--160},
+  year={2014}
+}

paper/paper.md

@@ -30,18 +30,19 @@ Despite the volume of research, studies often analyze only a limited subset of v
 
 `pyaesthetics` is a Python package for estimating visual features from still images. The package addresses the lack of available free, open-source, and easy-to-use tools for estimating a wide range of visual features. The API for `pyaesthetics` was designed to provide modules for various visual features commonly used in empirical aesthetics studies. It also offers simple entry points for conducting automated analysis for users with limited coding knowledge.
 
-Among the features, `pyaesthetics` allows for the estimation of brightness, contrast, saturation, visual complexity, symmetry, colorfulness, and color distribution. The updated list of features that can be estimated with `pyaesthetics` is available in the [repository of the project](https://github.com/Gabrock94/pyaesthetics) as well as in the [documentation of the project](https://prettywebsite.readthedocs.io/en/latest/index.html).
+Among the features, `pyaesthetics` allows for the estimation of brightness, contrast, saturation, visual complexity, symmetry, colorfulness, and color distribution. The updated list of features that can be estimated with `pyaesthetics` is available in the [repository of the project](https://github.com/Gabrock94/pyaesthetics) as well as in the [documentation of the project](https://prettywebsite.readthedocs.io/en/latest/index.html). `pyaesthetics` can be used to extract single or multiple features from images, and for features for which visualization is of help in interpreting the results (e.g. visual complexity by quadratic tree decomposition),  plotting utilities are provided (e.g., Figure 1).
 
-`pyaesthetics` is primarily aimed at researchers in the field of empirical aesthetics. However, its modules can also be useful for researchers in the Social Sciences, particularly Psychology, and Neuroscience, to explore the visual properties of stimuli used in various research projects. Additionally, `pyaesthetics` may be employed by visual designers, artists, and other individuals who need to analyze the visual properties of diverse images.
 
-The package has already been utilized in several peer-reviewed scientific publications. The code was used by @bizzego2022dataset and by @liu2024mothers to verify that the visual complexity of different visual stimuli was consistent across trials, by @gabrieli2023machine to investigate the effect of different visual features of still images of websites on participants' aesthetic judgments of the websites, by @cianfanelli2023binding to investigate the visual complexity of IAPS images, by @music2023beautification to investigate which visual features concurr in the perception of beaty of images. Moreover, the package was employed by @Veldhuizen2024 to investigate which aesthetic features are more important in influencing consumers’ aesthetic judgments of packaging labels.
 
+![Sample of the figure generated by `pyaesthetics`'s plotting utilities. The picture depicts the analysis of the visual complexity, estimated via quadratic tree decomposition, of a still image of a webiste from the AVI14 dataset [@miniukovich2014quantification]. Each square is a leaf of the decoposition tree, with the total number of leaves providing a measure of visual complexity, such that the higher the number of leaves, the more visual complex an image is. The visual representation allows for the understanding of which parts of the images contributes the most to the overall visual complexity of an image.}](QTDsample.png){ width=100% }
+
+`pyaesthetics` is primarily aimed at researchers in the field of empirical aesthetics. However, its modules can also be useful for researchers in the Social Sciences, particularly Psychology, and Neuroscience, to explore the visual properties of stimuli used in various research projects. Additionally, `pyaesthetics` may be employed by visual designers, artists, and other individuals who need to analyze the visual properties of diverse images.
 
 # Ease of Use
 
 In order to simplify the usage of `pyaesthetics`, the package provides a module that automatyze the estimation of all the possible features available. This modulus allows users who are not familiar with python or any other coding language to easily integrate `pyaesthetics` in their workflow. The module provide functions to conduct a complete analysis with default parameters that are suitable for a variety of applications. 
 
-An example of usage of the automated analysis module is reported below:
+An example of usage of the automated analysis module is reported below.
 
 ```python
 import pyaesthetics
@@ -53,6 +54,11 @@ path_to_img = "/path/to/image/image.jpg"
 results = pyaesthetics.analysis.analyzeImage(path_to_img, method="complete")
 ```
 
+# Use in scientific publications
+
+The package has already been utilized in several peer-reviewed scientific publications. The code was used by @bizzego2022dataset and by @liu2024mothers to verify that the visual complexity of different visual stimuli was consistent across trials, by @gabrieli2023machine to investigate the effect of different visual features of still images of websites on participants' aesthetic judgments of the websites, by @cianfanelli2023binding to investigate the visual complexity of IAPS images, by @music2023beautification to investigate which visual features concurr in the perception of beaty of images. Moreover, the package was employed by @Veldhuizen2024 to investigate which aesthetic features are more important in influencing consumers’ aesthetic judgments of packaging labels.
+
+
 # Acknowledgements
 
 We acknowledge support from Gianluca Esposito and Giulia Scapin during the genesis of this project, and Marith Veldhuizen for her inputs about the introduction of novel features.