Title :
Bagging in computer vision
Author :
Draper, Bruce A. ; Baek, Kyungim
Author_Institution :
Dept. of Comput. Sci., Colorado State Univ., Fort Collins, CO, USA
Abstract :
Previous research has shown that aggregated predictors improve the performance of non-parametric function approximation techniques. This paper presents the results of applying aggregated predictors to a computer vision problem, and shows that the method of bagging significantly improves performance. In fact, the results are better than those previously reported on other domains. This paper explains this performance in terms of the variance and bias
Keywords :
computer vision; function approximation; neural nets; object recognition; prediction theory; aggregated predictors; bagging; bias; computer vision; nonparametric function approximation techniques; variance; Application software; Bagging; Computer science; Computer vision; Function approximation; Navigation; Neural networks; Object recognition; Pattern recognition; Target recognition;
Conference_Titel :
Computer Vision and Pattern Recognition, 1998. Proceedings. 1998 IEEE Computer Society Conference on
Conference_Location :
Santa Barbara, CA
Print_ISBN :
0-8186-8497-6
DOI :
10.1109/CVPR.1998.698601
