A Cost Constrained Boosting Algorithm for Fast Object Detection
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Abstract
Boosting methods are among the most widely used machine learning techniques in practice for various reasons. In many scenarios, however, their use is prevented by runtime constraints. In this paper we propose a novel technique for reducing the computational complexity of hierarchical classifiers based on AdaBoost, such as the probabilistic boosting tree, which are often used for object detection. We modify AdaBoost training so that the hypothesis generation is no longer based solely on the weak learner’s training error but also on a measure of hypothesis complexity. This is achieved by incorporating a cost function into the optimization process, effectively constraining feature selection, which leads to a reduced overall classifier complexity and thus shorter evaluation times. The validity of the approach is shown in an experimental valuation on real-world data. In a cross validation experiment with a system for automatic segmentation of liver tumors in CT images, the evaluation cost for classifying previously unseen samples could be reduced by up to 76% using the methods described here without losing classification accuracy.