Reinforced AdaBoost Learning for Object Detection with Local Pattern Representations
Joint Authors
Lee, Younghyun
Han, David K.
Ko, Hanseok
Source
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-11-28
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
A reinforced AdaBoost learning algorithm is proposed for object detection with local pattern representations.
In implementing Adaboost learning, the proposed algorithm employs an exponential criterion as a cost function and Newton’s method for its optimization.
In particular, we introduce an optimal selection of weak classifiers minimizing the cost function and derive the reinforced predictions based on a judicial confidence estimate to determine the classification results.
The weak classifier of the proposed method produces real-valued predictions while that of the conventional Adaboost method produces integer valued predictions of +1 or −1.
Hence, in the conventional learning algorithms, the entire sample weights are updated by the same rate.
On the contrary, the proposed learning algorithm allows the sample weights to be updated individually depending on the confidence level of each weak classifier prediction, thereby reducing the number of weak classifier iterations for convergence.
Experimental classification performance on human face and license plate images confirm that the proposed method requires smaller number of weak classifiers than the conventional learning algorithm, resulting in higher learning and faster classification rates.
An object detector implemented based on the proposed learning algorithm yields better performance in field tests in terms of higher detection rate with lower false positives than that of the conventional learning algorithm.
American Psychological Association (APA)
Lee, Younghyun& Han, David K.& Ko, Hanseok. 2013. Reinforced AdaBoost Learning for Object Detection with Local Pattern Representations. The Scientific World Journal،Vol. 2013, no. 2013, pp.1-14.
https://search.emarefa.net/detail/BIM-1011545
Modern Language Association (MLA)
Lee, Younghyun…[et al.]. Reinforced AdaBoost Learning for Object Detection with Local Pattern Representations. The Scientific World Journal No. 2013 (2013), pp.1-14.
https://search.emarefa.net/detail/BIM-1011545
American Medical Association (AMA)
Lee, Younghyun& Han, David K.& Ko, Hanseok. Reinforced AdaBoost Learning for Object Detection with Local Pattern Representations. The Scientific World Journal. 2013. Vol. 2013, no. 2013, pp.1-14.
https://search.emarefa.net/detail/BIM-1011545
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1011545