An introduction to computational learning theory
An introduction to computational learning theory
Robust Real-Time Face Detection
International Journal of Computer Vision
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
S-adaboost and pattern detection in complex environment
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
A method for optimal division of data sets for use in neural networks
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
Detecting symmetry and symmetric constellations of features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
PCA enhanced training data for adaboost
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
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This paper describes a method to minimize the immense training time of the conventional Adaboost learning algorithm in object detection by reducing the sampling area. A new algorithm with respect to the geometric and accordingly the symmetric relations of the analyzed object is presented. Symmetry enhanced Adaboost (SEAdaboost) can limit the scanning area enormously, depending on the degree of the objects symmetry, while it maintains the detection rate. SEAdaboost allows to take advantage of the symmetric characteristics of an object by concentrating on corresponding symmetry features during the detection of weak classifiers. In our experiments we gain 39% reduced training time (in average) with slightly increasing detection rates (up to 2.4% and up to 6% depending on the object class) compared to the conventional Adaboost algorithm.