Matlab tools for "Predicting human gaze beyond pixels," Journal of Vision, 2014
Juan Xu, Ming Jiang, Shuo Wang, Mohan Kankanhalli, 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/computeMouseFixationMaps.m generates the mouse-tracking maps.
- src/dataset/showEyeData.m visualises the scanpaths of a given subject.
- src/metric/computeInterSubjectAUC.m computes the ideal (inter-subject) AUC scores.
- src/metric/normalizedAUC.m computes the normalized AUC scores.
- src/model/computeIttiMaps.m computes the pixel-level feature maps (Itti & Koch model).
- src/model/extractObjectFeatures.m computes the object level feature values.
- src/model/computeObjectMaps.m computes the object-level feature maps.
- src/model/computeSemanticMaps.m computes the semantic-level feature maps.
- src/model/splitData.m splits the data into training and testing sets.
- 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/mouse_amt/*.mat mouse-tracking data (sample points collected with crowdsourcing)
- data/mouse_lab/*.mat mouse-tracking data (sample points collacted in lab)
- data/attrs.mat manually labelled object masks and attributes
- lib/gbvs Graph-Based Visual Saliency http://www.vision.caltech.edu/~harel/share/gbvs.php
- lib/liblinear LIBLINEAR http://www.csie.ntu.edu.tw/~cjlin/liblinear/
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:
Juan Xu at jxu@nus.edu.sg
Ming Jiang at mjiang@nus.edu.sg