Explainable Visual Aesthetics(EVA) Dataset
This Explainable Visual Aesthetics(EVA) Dataset contains 5 csv files and the resized images from AVA dataset that were shown in our experiment.
If you want to use EVA dataset, please cite our paper "EVA: An Explainable Visual Aesthetics Dataset" by Chen Kang, Giuseppe Valenzise, Frédéric Dufaux in Joint Workshop on Aesthetic and Technical Quality Assessment of Multimedia and Media Analytics for Societal Trends (ATQAM/MAST'20).
The method, filter method and basic summary can be found in the paper. All of the processings we did are in python 3.6 and MATLAB.
This folder contains seven zip files called EVA_together.zip.00X for X=1,...,7. To unzip the pictures, concatenate the zip files into a single zip file before unzipping it, ie
cd images
cat EVA_together.zip.001 EVA_together.zip.002 EVA_together.zip.003 EVA_together.zip.004 EVA_together.zip.005 EVA_together.zip.006 EVA_together.zip.007 > EVA_together.zip
unzip EVA_together.zip
the resized images we selected from AVA dataset and showed to subjects.
You can use our classification of image category in image_content_category.csv or use your own classification. Notice the images are 600-700MB.
If you cannot download it successfully, you can go to https://mycore.core-cloud.net/index.php/s/ogTxYkJLrDr9x9Q for the two zip image files. EVA_category.zip contains images classified to different folders; EVA_together.zip contains all the images.
The first column is the image id, which is their names in AVA dataset.
The second column is the content 6 categories. Each image only belongs to one category. The classification method is described in the paper.
The delimiter is "=".
id: user's id
age: user's birth year
region: user's region. The region list is in "region_index.csv"
photographic_level_id: '1':'Beginner','2':'Amateur','0':'none','3':'Professional'
gender_id: '1':'male','2':'female'
eyecheck: '1':'glasses','2':'colour blind','0':'none','1,2':'both'
Among them, 'E1773','C76','C77','E148','E1389','E1248','E2261', 'E2340', 'E150', 'E1853', 'E1798','E2334'and 'E2316'are the users that we can rely on their honesty in voting.
The delimiter is "=".
image_id: image names without '.jpg'
user_id: user id. It is related with the "id" in "users.csv".
score: general score, the integer ranges in [0,10].
difficulty: very difficult=1, difficult=2, easy=3, very easy=4
visual, composition, quality, semantic: the attributes, very bad=1, bad=2, good=3, very good=4
factor: light and colour=1, composition and depth=2, quality=3, semantic=4
device: the browser information
vote_time: the voting time from last vote's submittion to this vote's submittion in seconds.
These are filtered votes we used.
The delimiter is "=".
image_id: image names without '.jpg'
user_id: user id. It is related with the "id" in "users.csv".
score: general score, integer ranges in [0,10]
difficulty: very difficult=1, difficult=2, easy=3, very easy=4
visual, composition, quality, semantic: the attributes, very bad=1, bad=2, good=3, very good=4
vote_time: the voting time from last vote's submittion to this vote's submittion.
1,2,3,4: 1=light and colour, 2=composition and depth, 3=quality, 4=semantic; value 1 means this checkbox is clicked, value 0 means it is not.
This is the region list and their codes used in users.csv.
The delimiter is "=".The first column is the codes, and the second column is the regions.
Analysis between AVA and EVA will be updated.