Abstract.
NormalBoost is a new boosting algorithm which is capable of classifying a
multi-dimensional binary class dataset. It adaptively combines several weak
classifiers to form a strong classifier. Unlike many boosting algorithms
which have high computation and memory complexities, NormalBoost is capable
of classification with low complexity.
The purpose of this paper is to present NormalBoost as a framework which
establishes a platform to solve classification problems. The approach was
tested with a dataset which was extracted automatically from real-world
traffic sign images. The dataset contains both images of traffic sign
borders and speed limit pictograms. This framework involves the computation
of Haar-like features of these images and then employs the NormalBoost classifier to classify these traffic signs. The total number of images which
were classified was 6500 binary images. A 
-fold validation was invoked to
check the validity of the classification which resulted in a classification
rate of 98.4% and 98.9% being achieved for these two databases. This
framework is distinguished by its invariance to in-plane rotation of the
images under consideration. Experiments show that the classification rate
remains almost constant when traffic sign images with different angles of
rotations were tested.
© 2012 by Walter de Gruyter Berlin Boston
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