This is the Matlab saliency code able to process saliency maps and metrics.
Run metrics from "input/", Example for test dataset:
main('test',0);
This will export a csv in "output/" (main.m automatically runs see_results.m after storing results).
Images, fixation maps and saliency maps should be saved in the following directories (by default):
input/images/DATASET_NAME
input/bmaps/DATASET_NAME
input/dmaps/DATASET_NAME
input/smaps/DATASET_NAME/MODEL_NAME
Although only necessary folders for computing saliency metrics are "images", "bmaps", "dmaps" and "smaps". Other metrics require region binary masks "mmaps" or scanpaths ("input/smaps/DATASET_NAME/scanpaths" for GT, "input/smaps/DATASET_NAME/MODEL_NAME/scanpaths" for model scanpaths).
To run any dataset, copy your dataset files and make sure you have the same folder structure as in "test".
Run saliency maps from "models/", Example for test dataset:
get_smaps('models',{'test'});
Before running saliency models, first try to delete them, e.g. "input/smaps/test/SIM"
Note: To add another model, make sure each model runs a matlab file with same format (input and output of function) and prefix "saliency_MODEL_NAME" as in "saliency_sim.m". You can also run shell commands through matlab for python-based models. We have included an example code of all saliency models to make your tests (try to run install.sh in every subfolder before running them).
Download GT: Use the shell commands (download_parse_datasets/DATASET_NAME/download.sh) for downloading and moving eye-tracking data (images, binary fixation maps, fixation density maps and scanpaths) of previous experiments of Toronto (Bruce & Tsotsos, 2006), MIT1003 (Judd et al., 2009), KTH (Kootstra et al., 2011), CAT2000 Pattern (Borji & Itti, 2015) and SID4VAM (Berga et al., 2019).
(or) Download and parse GT: Here are the parsers for ground truth data for each raw data from previous datasets.
(or) Download the already parsed ground truth: Here
You can download the saliency maps from my owncloud.
Forked code from https://github.com/cvzoya/saliency is found in "include/saliency-master/"
This specific benchmark is part of work done in the following papers, please cite:
@inproceedings{9008799,
author={Berga, David and Vidal, Xosé Ramón Fernández and Otazu, Xavier and Pardo, Xosé M.},
booktitle={2019 IEEE/CVF International Conference on Computer Vision (ICCV)},
title={SID4VAM: A Benchmark Dataset With Synthetic Images for Visual Attention Modeling},
year={2019},
volume={},
number={},
pages={8788-8797},
doi={10.1109/ICCV.2019.00888}
}
@article{10.1162/neco_a_01464,
author = {Berga, David and Otazu, Xavier},
title = "{A Neurodynamic Model of Saliency Prediction in V1}",
journal = {Neural Computation},
volume = {34},
number = {2},
pages = {378-414},
year = {2022},
month = {01},
issn = {0899-7667},
doi = {10.1162/neco_a_01464},
url = {https://doi.org/10.1162/neco\_a\_01464},
eprint = {https://direct.mit.edu/neco/article-pdf/34/2/378/1982874/neco\_a\_01464.pdf},
}
Various code related to the MIT saliency benchmark website http://saliency.mit.edu will be found there. Please contact saliency@mit.edu with any questions. If you use any of this code, please cite:
@article{salMetrics_Bylinskii,
title = {What do different evaluation metrics tell us about saliency models?},
author = {Zoya Bylinskii and Tilke Judd and Aude Oliva and Antonio Torralba and Fr{\'e}do Durand},
journal = {arXiv preprint arXiv:1604.03605},
year = {2016}
}