International Journal of Computer Vision
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Recognition without Correspondence using MultidimensionalReceptive Field Histograms
International Journal of Computer Vision
Finding Line Segments by Stick Growing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Improvement of Visual Detectors using Co-Training
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Improvement of Boosting Algorithm by Modifying the Weighting Rule
Annals of Mathematics and Artificial Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Random Subwindows for Robust Image Classification
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Efficient Image Matching with Distributions of Local Invariant Features
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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In this paper, we propose a novel visual learning framework to develop flexible and accurate object recognition methods. Currently, most of visual learning based recognition methods adopt the monostrategy learning framework using a single feature. However, the real-world objects are so complex that it is quite difficult for monostrategy method to correctly classify them. Thus, utilizing a wide variety of features is required to precisely distinguish them. In order to utilize various features, we propose multistrategical visual learning by integrating multiple visual learners. In our method, multiple visual learners are collaboratively trained. Specifically, a visual learner L intensively learns the examples misclassified by the other visual learners. Instead, the other visual learners learn the examples misclassified by L. As a result, a powerful object recognition method can be developed by integrating various visual learners even if they have mediocre recognition performance.