Reinforced AdaBoost Learning for Object Detection with Local Pattern Representations
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...
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2013/153465 |
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| author | Younghyun Lee David K. Han Hanseok Ko |
| author_facet | Younghyun Lee David K. Han Hanseok Ko |
| author_sort | Younghyun Lee |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f17ea5d0713b4f25a879046ac5609754 |
| institution | Kabale University |
| issn | 1537-744X |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-f17ea5d0713b4f25a879046ac56097542025-02-03T05:57:27ZengWileyThe Scientific World Journal1537-744X2013-01-01201310.1155/2013/153465153465Reinforced AdaBoost Learning for Object Detection with Local Pattern RepresentationsYounghyun Lee0David K. Han1Hanseok Ko2Department of Visual Information Processing, Korea University, Anam-dong, Seongbuk-gu, Seoul 136-713, Republic of KoreaOffice of Naval Research, Arlington, VA 22203, USASchool of Electrical Engineering, Korea University, Engineering Buliding, Room 419, Anam-dong, Seongbuk-gu, Seoul 136-713, Republic of KoreaA 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.http://dx.doi.org/10.1155/2013/153465 |
| spellingShingle | Younghyun Lee David K. Han Hanseok Ko Reinforced AdaBoost Learning for Object Detection with Local Pattern Representations The Scientific World Journal |
| title | Reinforced AdaBoost Learning for Object Detection with Local Pattern Representations |
| title_full | Reinforced AdaBoost Learning for Object Detection with Local Pattern Representations |
| title_fullStr | Reinforced AdaBoost Learning for Object Detection with Local Pattern Representations |
| title_full_unstemmed | Reinforced AdaBoost Learning for Object Detection with Local Pattern Representations |
| title_short | Reinforced AdaBoost Learning for Object Detection with Local Pattern Representations |
| title_sort | reinforced adaboost learning for object detection with local pattern representations |
| url | http://dx.doi.org/10.1155/2013/153465 |
| work_keys_str_mv | AT younghyunlee reinforcedadaboostlearningforobjectdetectionwithlocalpatternrepresentations AT davidkhan reinforcedadaboostlearningforobjectdetectionwithlocalpatternrepresentations AT hanseokko reinforcedadaboostlearningforobjectdetectionwithlocalpatternrepresentations |