UMR CNRS 7253

Philippe Xu
Philippe Xu
Philippe Xu
Philippe Xu
Philippe Xu

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Evidential Calibration

We provide the MATLAB® code for our evidential multiclass classifier calibration method.

  • calibTrain.m: train the calibration model given some validation data
  • score2prob.m: transform a vector of scores into probabilities
  • score2plaus.m: transform a vector of scores into plausibilities over singletons
Download
Link
Calibration code multinomial_calibration.zip
References

Ph. Xu, F. Davoine, H. Zha and T. Denœux. Evidential calibration of binary SVM classifiers. International Journal of Approximate Reasoning (IJAR), Vol. 72, pages 55–70, May 2016.
Paper HAL DOI

Ph. Xu, F. Davoine and T. Denœux. Evidential Multinomial Logistic Regression for Multiclass Classifier Calibration. In Proceedings of the 18th International Conference on Information Fusion, pages 1106-1112, Washington, D.C., July 6-9, 2015.
Paper

Ph. Xu, F. Davoine and T. Denœux. Evidential Logistic Regression for Binary SVM Classifier Calibration. In F. Cuzzolin, editor, Belief Functions: Theory and Applications. Proceedings of the 3rd International Conference on Belief Functions, Springer, LNCS 8764, pages 49-57, Oxford, UK, September 26-28, 2014.
Paper Oral DOI BibTeX


Combination of pedestrian detectors

We provide the MATLAB® code for our evidential combination of pedestrian detectors.

To make the code work, you will need to download the Matlab evaluation/labeling code provided by the Caltech Pedestrian Detection Benchmark and Piotr's Matlab Toolbox. Copy the dbFusion.m file in the same directory as the dbEval.m file and simply run it. To divide the UsaTest dataset into a validation and testing set, add the following line to dbInfo.m:

case 'usaval' % Caltech Pedestrian Datasets (validation)
  setIds=6; subdir='USA'; skip=30; ext='jpg';
  vidIds={0:18};
case 'usatest2' % Caltech Pedestrian Datasets (testing sub set)
  setIds=7:10; subdir='USA'; skip=30; ext='jpg';
  vidIds={0:11 0:10 0:11 0:11};
Download
Link
Combination code dbfusion.zip
Caltech evaluation code Link
Piotr's Matlab Toolbox Link
References

Ph. Xu, F. Davoine and T. Denoeux. Evidential Combination of Pedestrian Detectors. In Proceedings of the 25th British Machine Vision Conference (BMVC), Nottingham, UK, September 1-5, 2014. paper oral


KITTI semantic segmentation

A set of 107 images (70 for training and 37 for testing) from the KITTI Vision Benchmark Suite were manually annotated with the software Adobe® Photoshop® CS2. The left color images were annotated at the pixel level considering a set of 28 classes.

Download
Training set Testing set
Ground truth gttrain.zip gttest.zip
Left images lefttrain.zip lefttest.zip
Right images righttrain.zip righttest.zip
Velodyne data velotrain.zip velotest.zip

For convenience, we also provide the left and right images, as well as the Velodyne data, associated to the ground truth annotations. These data were extracted from the raw sequences. They are copyright by the KITTI Vision Benchmark Suite and published under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License.

References

Ph. Xu, F. Davoine, J.-B. Bordes, H. Zhao and T. Denœux. Multimodal Information Fusion for Urban Scene Understanding. Machine Vision and Applications (MVA), Vol. 27, Issue 3, pages 331–349, April 2016.
HAL DOI

Ph. Xu, F. Davoine, J.-B. Bordes, H. Zhao and T. Denoeux. Information Fusion on Oversegmented Images: An Application for Urban Scene Understanding. In Proceedings of the Thirteenth IAPR International Conference on Machine Vision Applications (MVA), pages 189-193, Kyoto, Japan, May 20-23, 2013. paperoral HAL


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