Implements a regression model for predicting the performance gain of a bootstrapping process prior to actually applying it. Computes the two features; baseline classifier maturity and oracle reliability.
Based on paper "PERFORMANCE PREDICTION OF BOOTSTRAPPING FOR IMAGE CLASSIFICATION", Chatzilari et al.
Run PerformancePrediction.m to reproduce the results of the paper:
Input - Calculate features from visual and textual kernel
VisualAnnotationFileName & TextualAnnotationFileName: Files containing the training set annotations; anns: matrix of dimensions #training instances x # concepts with '1's wherever an image is positive for a concept and -1 otherwise.
VisualKernelFileName file including the training kernel for calculating the maturity feature - based on visual features
TextualKernelFileName: file including the training kernel for calculating the reliability feature - based on the textual features, i.e. oracle
Input - Give features as input
maturityFileName: file including the maturity of a classifier as a matrix of dimensions #concepts x # CV folds (matrix name should be AP)
maturityFileName: file including the reliability of a classifier as a matrix of dimensions #concepts x # CV folds (matrix name should be AP)
Results_Initial.mat & Results_Final.mat: files containing the average precision of the baseline and enhanced classifiers respectively. They include a vector AP of dimension #concepts x 1
Download the data for a demo run from: http://mklab.iti.gr/project/PerformancePrediction
PRE-REQUIREMENTS: Runs with matlab version of the libsvm library: the svmtrain and svmpredict mex files should be added in the matlab path
Tested on Matlab 2012a
For any comments, questions, suggestions contact: Elisavet Chatzilari ehatzi@iti.gr