The CapMIT1003 database contains captions and clicks collected for images from the MIT1003 database, for which reference eye scanpath are available. The database is distributed as a single SQLite3 database named capmit1003.db
. For convenience, a lightweight Python class to access the database is provided (see "Programmatic Usage").
Name | Type | Description |
---|---|---|
obs_uid | String | Unique identifier for a labeled image-caption pair. |
usr_uid | String | Unique identifier for a single user. |
start_time | Timestamp | Date and time (absolute) at which image was shown to user. |
caption | String | Caption provided by the user. |
img_uid | String | Unique identifier for a single image from MIT1003. |
img_path | String | File name of image from MIT1003. |
click_id | Integer | Ascending identifier that may be used to order clicks. |
x | Integer | Horizontal position of click in image pixel coordinates. |
y | Integer | Vertical position of click in image pixel coordinates. |
click_time | Timestamp | Date and time (absolute) at which user clicked on image. |
The file capmit1003.py
provides a CapMIT1003
dataset class to query the SQLite3 database. Its only dependency is pandas, a popular library for handling tabular datasets. The following snippet demonstrates how to iterate over all image-caption pairs and load the image, caption and click path. In addition, it downloads and extracts the MIT1003 stimuli images if they are not already present.
from capmit1003 import CapMIT1003
CapMIT1003.download_images()
with CapMIT1003('capmit1003.db') as db:
image_captions = db.get_captions()
for pair in image_captions.itertuples(index=False):
image = imread(pair.img_path) # e.g., using Pillow, scikit-image, etc.
caption = pair.caption
click_path = db.get_click_path(pair.obs_uid)
xy_coordinates = click_path[['x', 'y']].values
Note the usage with with
; this ensures that the database is properly closed after all queries.
The file main.py
provide an example to simulate scanpaths with NevaClip, using the caption encoding as guidance.
When using the dataset, please make sure to also cite the original MIT1003 database.
@InProceedings{Judd_2009,
author = {Tilke Judd and Krista Ehinger and Fr{\'e}do Durand and Antonio Torralba},
title = {Learning to Predict Where Humans Look},
booktitle = {IEEE International Conference on Computer Vision (ICCV)},
year = {2009}
}