The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Probabilistic boosting-tree: learning discriminative models for classification, recognition, and clustering
Abstract
In this paper, a new learning framework - probabilistic boosting-tree (PBT), is proposed for learning two-class and multi-class discriminative models. In the learning stage, the probabilistic boosting-tree automatically constructs a tree in which each node combines a number of weak classifiers (evidence, knowledge,) into a strong classifier (a conditional posterior probability). It approaches the target posterior distribution by data augmentation (tree expansion) through a divide-and-conquer strategy. In the testing stage, the conditional probability is computed at each tree node based on the learned classifier, which guides the probability propagation in its sub-trees. The top node of the tree therefore outputs the overall posterior probability by integrating the probabilities gathered from its sub-trees. Also, clustering is naturally embedded in the learning phase and each sub-tree represents a cluster of certain level. The proposed framework is very general and it has interesting connections to a number of existing methods such as the A* algorithm, decision tree algorithms, generative models, and cascade approaches. In this paper, we show the applications of PBT for classification, detection, and object recognition. We have also applied the framework in segmentation
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Arbeláez, P., Maire, M., Fowlkes, C. C., & Malik, J. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.161 · Cited by 5,581
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Panayides, A. S., Amini, A., Filipovic, N. D., Sharma, A., Tsaftaris, S. A., Young, A., Foran, D., Do, N., Golemati, S., Kurc, T., et al. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2991043 · Cited by 713
Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation
Tu, Z., & Bai, X. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.186 · Cited by 578
Metadata
- Authors
- Tu, Z.
- Venue
- Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1
- Publisher
- IEEE
- Published
- 2005
- Pages
- 1589-1596 Vol. 2
- OpenAlex
- W2146514558
- Cited by
- 397
- References
- 23
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 3 · 2023: 1 · 2022: 6 · 2021: 5 · 2020: 4 · 2019: 13 · 2018: 8 · 2017: 15 · 2016: 16 · 2015: 27 · 2014: 33 · 2013: 31 · 2012: 33
- FWCI
- 16.11
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Keywords
- discriminative models, weak classifiers, data augmentation, divide-and-conquer, probability propagation, multi-class classification, clustering, object recognition, object detection, image segmentation
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