Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Survival Tree

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Automated, anatomy-based, heuristic post-processing reduces false positives and improves interpretability of deep learning intracranial aneurysm detection models.

Scientific reports·2025

Same author

Toward Efficient Semi-Supervised Object Detection With Detection Transformer.

IEEE transactions on pattern analysis and machine intelligence·2025

Same author

Improving Generalized Visual Grounding With Instance-Aware Joint Learning.

IEEE transactions on pattern analysis and machine intelligence·2025

Same author

Surfel-Based Gaussian Inverse Rendering for Fast and Relightable Dynamic Human Reconstruction From Monocular Videos.

IEEE transactions on pattern analysis and machine intelligence·2025

Same author

DREAM: A Dual Variational Framework for Unsupervised Graph Domain Adaptation.

IEEE transactions on pattern analysis and machine intelligence·2025

Same author

Photovoltaic panel defect detection algorithm based on infrared imaging and improved YOLOv8.

PeerJ. Computer science·2025

Same journal

Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

GoP-based Quality Enhancement on Video Compression.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

Align then Tensorize: Multi-Level Consistent Anchor Graph Learning for Scalable Multi-View Clustering.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

Beyond Fidelity: Diverse Image Synthesis via Retrieval-Augmented Diffusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 30, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Regularized tree partitioning and its application to unsupervised image segmentation.

Jingdong Wang, Huaizu Jiang, Yangqing Jia

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|May 9, 2014

Summary

This summary is machine-generated.

We introduce regularized tree partitioning methods, normalized tree partitioning (NTP) and average tree partitioning (ATP), for efficient image segmentation. These approaches demonstrate effectiveness compared to state-of-the-art techniques.

More Related Videos

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Related Experiment Videos

Last Updated: Apr 30, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Area of Science:

Computer Vision
Machine Learning
Graph Theory

Background:

Image segmentation is a critical task in computer vision.
Existing methods for image segmentation often face challenges in efficiency and accuracy.
Graph-based partitioning methods, like normalized cut (NCut) and average cut (ACut), are powerful but can be computationally intensive.

Purpose of the Study:

To propose novel regularized tree partitioning approaches for image segmentation.
To develop efficient algorithms for these new partitioning methods.
To analyze the theoretical properties and relationships of these methods with existing graph-cut criteria.

Main Methods:

Introduced normalized tree partitioning (NTP) and average tree partitioning (ATP) criteria over trees.
Developed properties for efficient algorithms applicable to both NTP and ATP.
Investigated the relationship between NTP/ATP solutions and graph-based NCut/ACut via maximum weight spanning trees.

Main Results:

Established efficient algorithms for NTP and ATP.
Demonstrated the effectiveness of NTP and ATP in image segmentation tasks.
Provided theoretical insights into the connection between tree partitioning and graph cuts.

Conclusions:

The proposed regularized tree partitioning methods (NTP and ATP) offer an efficient and effective approach to image segmentation.
These methods show promise for advancing the state-of-the-art in image segmentation.
The theoretical analysis provides a deeper understanding of graph partitioning techniques.