Title :
A paper currency number recognition based on fast Adaboost training algorithm
Author :
Wang, Hai-dong ; Gu, Leye ; Du, Linping
Author_Institution :
Chengdu Comput. Applic. Inst., Chinese Acad. of Sci. Technol. of Comput. Applic., Chengdu, China
Abstract :
As Adaboost has a good performance in classification, it is widely used in pattern recognition. However it takes long time to generate the weak classifiers. For improving the training speed, this paper presents a fast Adaboost weak classifier training algorithm. Firstly, sort the Eigen values to an array from small to large, and then traverse the sorted array once to find the best threshold and bias. The experimental results show that this improved algorithm can increase the training speed by 6 to 8 times.
Keywords :
eigenvalues and eigenfunctions; image classification; image recognition; learning (artificial intelligence); classification performance; eigenvalues; fast Adaboost weak classifier training algorithm; paper currency number recognition; pattern recognition; sorted array; Arrays; Binary trees; Classification algorithms; Computer applications; Face detection; Pattern recognition; Training; fast training algorithm; optimal threshold; weak classifier;
Conference_Titel :
Multimedia Technology (ICMT), 2011 International Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-61284-771-9
DOI :
10.1109/ICMT.2011.6002079
