code for Geodesic Weighted Bayesian Model for Salient Object Detection
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CalGeoProb.m
CalImprovedMap.m
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README.md
demo.m
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smoothSalMaps.m

README.md

Geodesic Weighted Bayesian Model For Salient Object Detection (GWB) v2

Copyright 2015 Xiang Wang (wangxiang14@mails.tsinghua.edu.cn)

Introduction


This is a accelerated version of the original code: http://3dimage.ee.tsinghua.edu.cn/files/XiangWang/gwb/GWB_ICIP15_code.zip The code can be further speeded up by using mexopencv to compute the histogram. https://github.com/kyamagu/mexopencv


GWB can be used to improve the quality of most existing salient object detection models with little computation overhead. If you use GWB, please cite the following paper:

@inproceedings{icip15gwb,
  author    = {Xiang Wang and Huimin Ma and Xiaozhi Chen},
  title     = {Geodesic Weighted Bayesian Model For Salient Object Detection},
  booktitle = {IEEE ICIP},
  year      = {2015},
}

Demo for GWB

GWB can be integrated into any existing salient object detection models.

Run 'demo.m' which using an image as source image and a saliency map as prior distribution,

this demo will generate a improved saliency map using GWB, and save it in the Result path.

A comparison will also be shown.

Improve your own saliency maps

To apply GWB to your own saliency maps , follow these steps:

  1. Edit 'demo_Improve.m' to add information of your own saliency map. Including the suffix of your maps' name (psalSuffix) and the name of your method (method);
  2. Put your saliency maps to the path SalMaps or modify it
  3. Put your source images to the path Src or modify it
  4. Run 'demo_Improve.m'.

Acknowledgements

We use or modify:

  • K. van de Sande's code for colorspaces conversion,
  • Wangjiang Zhu's code for calculating geodesic distance
  • Yulin Xie's code for calculating probabilities
  • P. Felzenszwalb's code for graph-based segmentation.