Active online confidence boosting for efficient object classification

We present a novel efficient algorithm for object classification. Our method is based on the active learning framework, in which training and classification are performed in loops, and new ground truth labels are queried from the supervisor in each loop. Our underlying classifier is from the family of boosting methods, but in contrast to earlier methods, our Confidence Boosting particularly focusses on misclassified samples that have a high classification confidence associated. We show that weighting these samples more than others leads to a decrease of overconfidence, for which we give a formal definition. As a result, our classifier is better suited for active learning, leading to steeper learning curves and less required label queries. We show the benefits of our approach on standard data sets from machine learning and robotics.

Authors

• Dennis Mund
• Rudolph Triebel
• Daniel Cremers

Keywords

• Boosting
• Uncertainty
• Training
• Robots
• Standards
• Histograms
• Training data
• Object Classification
• Confidence Boosting
• Active Learning
• Ground Truth Labels
• New Ground
• Classification Confidence
• Training Set
• Learning Process
• Support Vector Machine
• Running Time
• Class Labels
• Correct Classification
• Classification Error
• Classification Rate
• Confidence Threshold
• Gradient Boosting
• Label Prediction
• Weak Learners
• Passive Learning
• Classification Uncertainty
• Active Learning Methods
• Prediction Vector
• Predicted Class Label
• Learning Epochs
• False Classification
• Human Users
• Similar Estimates
• Computer Vision