This repository contains reference code for computing Ensembles of Deep Networks (eDN) saliency maps based on the CVPR'2014 paper "Large-Scale Optimization of Hierarchical Features for Saliency Prediction in Natural Images".
./eDNsaliency [--opts] <image> <output_saliency_map>
Options:
-h, --help show this help message and exit
--descs DESCPATH path to eDN model(s) (default: ./slmBestDescrCombi.pkl)
--svm SVMPATH path to SVM file (default: ./svm-slm-cntr)
--white WHITEPATH path to whitening parameters (default: ./whiten-slm-cntr)
--fixMap FIXMAP fixation map / empirical saliency map, if available
--histeq histogram equalization with given empirical saliency map
(default: False); requires empirical saliency map
--auc computes AUC for given fixation map; requires fixation map
--no-blur disable the default smoothing of the final map
Input format:
- fixation map: black image with fixated pixels (one per fixation) set to 255 (see ./img_fixPts.jpg)
- empirical saliency map: superposition of Gaussians centered at fixations (see ./img_fixMap.jpg)
These images should have the same size as the input image.
./eDNsaliency img.jpg salMap.jpg
Computes raw (non-histogram-equalized) saliency map (in salMap.jpg) for given image
./eDNsaliency --histeq --fixMap img_fixMap.jpg img.jpg salMap-histeq.jpg
Computes histogram-equalized saliency map with given empirical saliency map (img_fixMap.jpg)
./eDNsaliency --auc --fixMap img_fixPts.jpg img.jpg salMap.jpg
Computes Area Under the Curve (AUC) for fixation map (img_fixPts.jpg)
./eDNsaliency --svm ./svm-slm --white ./whiten-slm img.jpg salMap-noCntr.jpg
Computes non-centered saliency maps
sthor
liblinear
(Tested under Ubuntu 14.04)
- Install dependencies
sudo apt-get install python-matplotlib python-setuptools curl python-dev libxml2-dev libxslt-dev
- Install liblinear
Download toolbox from http://www.csie.ntu.edu.tw/~cjlin/liblinear/
# extract the zip
make
cd python
make
- Install sthor dependencies
curl -O http://python-distribute.org/distribute_setup.py
sudo python distribute_setup.py
sudo easy_install pip
sudo easy_install -U scikit-image
sudo easy_install -U cython
sudo easy_install -U numexpr
sudo easy_install -U scipy
For speedup, numpy and numexpr should be built against e.g. Intel MKL libraries.
- Install sthor
git clone https://github.com/nsf-ri-ubicv/sthor.git
cd sthor/sthor/operation
sudo make
cd ../..
python setup.py install
add the sthor directory and the liblinear/python directory to your PYTHONPATH
- Test sthor installation
python
import sthor # should import without errors
We provide precomputed saliency maps for three standard benchmarks:
- MIT data set: MIT1003_eDN.zip
- Toronto data set: Toronto_eDN.zip
- NUSEF data set: NUSEF_eDN.zip
If you use this code in your own work, please cite the following paper:
Eleonora Vig, Michael Dorr, David Cox, "Large-Scale Optimization of Hierarchical Features for Saliency Prediction in Natural Images", IEEE Computer Vision and Pattern Recognition (CVPR), 2014.
Link to the paper: http://coxlab.org/pdfs/cvpr2014_vig_saliency.pdf
For questions and feedback please contact me at [email protected]
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