No description, website, or topics provided.
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
data first release of the saliency codes Aug 4, 2014
lib add mex compilation code Aug 6, 2014
src Fix feature map error Aug 18, 2014
LICENSE Initial commit Aug 3, 2014
README.md updated read me Aug 5, 2014
demo.m add mex compilation code Aug 6, 2014

README.md

Saliency in Crowd

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.

Contents

Source Code

  • 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

  • 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)

Dependencies

Getting Started

Open Matlab and run demo.m to compute the fixation maps, the feature maps, to learn and evaluate the saliency model.

Contacts

Send feedback, suggestions and questions to:
Ming Jiang at mjiang@nus.edu.sg