BAGGING ALGORITHM, ONLINE BOOSTING AND VISION Se – Hoon Park.

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Presentation transcript:

BAGGING ALGORITHM, ONLINE BOOSTING AND VISION Se – Hoon Park

ENSEMBLE METHOD  Multiple ‘base’ models (classifiers, regressors), each covers a different part (region) of th e input space.

BAGGING ALGORITHM

 Given  Training set of N examples  A class of learning models(decision trees, neural networks, …)  Method  Train multiple(k) models on different samples(data splits)  Predict (test) by averaging or majority voting the results of k models  Goal  Improve the accuracy of one model by using its multiple copies  Average of misclassification errors on different data splits gives a better estimate of the predictive ability of a learning method

BAGGING ALGORITHM  Training  Randomly sample with replacement N samples from the training set  Train a chosen “base model”(neural network, decision tree) on the samples  Test  Start all trained base models  Predict by combining results of all trained models  Regression : averaging  Classification : a majority vote

BAGGING ALGORITHM  Bias vs variance Under fitting High bias Small variance Over fitting Small bias High variance

BAGGING ALGORITHM  Main property of bagging  Bagging decreases variance of the base model without changing the bias because of averaging  Bagging is useful when applied with an over-fitted base model  It does not help much  High bias, when the base model is robust to the changes in the training data

ONLINE BOOSTING AND VISION Helmut Grabner and Horst Bischof, CVPR 2006 Institute for Computer Graphics and Vision, Graz University of Technology

OFFLINE BOOSTING

ONLINE BOOSTING FOR FEATURE SELECTION

APPLICATION : BACKGROUND MODEL

APPLICATION : TRACKING

APPLICATION : OBJECT DETECTION  Detection using offline boosting  Trained classifier scan over the whole image at multiple locations and scales  Detection using online boosting  All patches where motion detection has detected an object are selected as positive examples.  10% false positives a robust reconstructive representation (PCA on appearance and shape) is computed from the output of the motion detector.  Thus, the false positives can be filtered out and may be used as negative examples

APPLICATION : OBJECT DETECTION  evaluation Initial classifierAfter 300 frameAfter 1200 frame TP : true positive FP : false positive nP : number of positive