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
Context-based vision system for place and object recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
PLSA-based image auto-annotation: constraining the latent space
Proceedings of the 12th annual ACM international conference on Multimedia
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Multi-class pattern classification using neural networks
Pattern Recognition
International Journal of Computer Vision
Representing shape with a spatial pyramid kernel
Proceedings of the 6th ACM international conference on Image and video retrieval
Scene Classification Using a Hybrid Generative/Discriminative Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-invariant visual language modeling for object categorization
IEEE Transactions on Multimedia - Special issue on integration of context and content
Object Categorization Using Hierarchical Wavelet Packet Texture Descriptors
ISM '09 Proceedings of the 2009 11th IEEE International Symposium on Multimedia
Scene categorization using boosted back-propagation neural networks
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
Multimedia Tools and Applications
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Scene categorization plays an important role in computer vision and image content understanding. It is a multi-class pattern classification problem. Usually, multi-class pattern classification can be completed by using several component classifiers. Each component classifier carries out discrimination of some patterns from the others. Due to the biases of training data, and local optimal of weak classifiers, some weak classifiers may not be well trained. Usually, some component classifiers of a weak classifier may be not act well as the others. This will make the performances of the weak classifier not as good as it should be. In this paper, the inner structures of weak classifiers are adjusted before their outer weights determination. Experimental results on three AdaBoost algorithms show the effectiveness of the proposed approach.