Matlab tools for "Saliency in crowd," ECCV, 2014
Ming Jiang, Juan Xu, Qi Zhao
Copyright (c) 2014 NUS VIP - Visual Information Processing Lab
Distributed under the MIT License. See LICENSE file in the distribution folder.
- demo.m: demonstrates the usage of this package.
- src/common/config.m defines the configuration parameters.
- src/common/normalise.m normalises a saliency map.
- src/dataset/computeFixationMaps.m generates the human fixation maps.
- src/dataset/showEyeData.m visualises the scanpaths of a given subject.
- src/metric/computeShuffleMap.m computes the shuffle map for sAUC evaluation.
- src/metric/evaluateSaliencyMaps.m evaluates the predicted saliency maps.
- src/model/collectFeatures.m collects features for training and testing.
- src/model/computeIttiMaps.m computes the pixel-level feature maps (Itti & Koch model).
- src/model/computeCrowdStats.m computes the crowd statistics (density level, size, density, etc.)
- src/model/computeFaceMaps.m computes the face feature maps.
- src/model/splitData.m splits the data into training and testing sets.
- src/model/sampling.m samples training data.
- src/model/training_mkl.m calls the simplemkl functions for model training.
- src/model/trainModel.m trains the saliency model.
- src/model/computeSaliencyMaps.m computes the predicted saliency maps.
- data/stimuli/*.jpg stimuli files
- data/eye/fixations.mat eye-tracking data (fixation points and durations)
- data/labels.mat manually labelled faces (including ROIs and attributes)
- lib/gbvs Graph-Based Visual Saliency http://www.vision.caltech.edu/~harel/share/gbvs.php
- lib/simplemkl SimpleMKL http://asi.insa-rouen.fr/enseignants/~arakoto/code/mklindex.html
Open Matlab and run demo.m to compute the fixation maps, the feature maps, to learn and evaluate the saliency model.
Send feedback, suggestions and questions to:
Ming Jiang at mjiang@nus.edu.sg