Probabilistic boosting-tree: learning discriminative models for classification, recognition, and clustering

Zhuowen Tu

doi:10.1109/iccv.2005.194

Cited by 397Advanced Neural Network ApplicationsAdvanced Image and Video Retrieval TechniquesBayesian Modeling and Causal Inference

Probabilistic boosting-tree: learning discriminative models for classification, recognition, and clustering

Zhuowen Tu·2005·Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1

DOI 10.1109/iccv.2005.194

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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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Authors: Tu, Z.
Venue: Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1
Publisher: IEEE
Published: 2005
Pages: 1589-1596 Vol. 2
DOI: 10.1109/iccv.2005.194
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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Probabilistic boosting-tree: learning discriminative models for classification, recognition, and clustering

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