Jove
Visualize
Contact Us

Related Experiment Videos

A cellular coevolutionary algorithm for image segmentation.

Cor J Veenman1, Marcel T Reinders, Eric Backer

  • 1Dept. of Mediamatics, Delft Univ. of Technol., Netherlands. C.J.Veenman@its.tudelft.nl

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 2, 2008
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Genetic testing of common and rare variants in dementia patients from a memory clinic : Dementia-related genetic testing in memory clinic.

Alzheimer's research & therapy·2025
Same author

Prediction of long-term hospitalisation and all-cause mortality in patients with chronic heart failure on Dutch claims data: a machine learning approach.

BMC medical informatics and decision making·2021
Same author

Finding Dutch natives in online forums.

Forensic sciences research·2018
Same author

Visual word ambiguity.

IEEE transactions on pattern analysis and machine intelligence·2010
Same author

Source camera identification for heavily JPEG compressed low resolution still images.

Journal of forensic sciences·2009
Same author

The nearest subclass classifier: a compromise between the nearest mean and nearest neighbor classifier.

IEEE transactions on pattern analysis and machine intelligence·2005
Same journal

Style-Aware Contrastive Test-Time Adaptation: A Dual-Cache Model for Robust Vision-Language Alignment.

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

Semantic Frame Interpolation.

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

Physics-Guided Cross-Modal Decoupling with Test-Time Adaptation for Hyperspectral Image Restoration.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
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
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

This study introduces a novel clustering model for image segmentation that does not require knowing the number of clusters beforehand. A cellular coevolutionary algorithm optimizes the model, demonstrating effective and efficient image segmentation capabilities.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Image Processing

Background:

  • Clustering is a challenging problem in data analysis and machine learning.
  • Traditional clustering methods often require prior knowledge of the number of clusters.
  • Image segmentation is a key application area where clustering is utilized.

Purpose of the Study:

  • To present a novel clustering model that does not require a priori knowledge of the number of clusters.
  • To address the specific challenges of image segmentation using this new model.
  • To propose an efficient optimization method for the clustering model.

Main Methods:

  • A cellular coevolutionary algorithm is proposed for model optimization.
  • Agents are arranged on a 2-D grid representing the image.

Related Experiment Videos

  • Agents cooperatively perform pixel migration to enhance region homogeneity and form alliances or isolate deviant subjects.

    • Main Results:

    • The proposed model effectively segments images without prior cluster count specification.
    • Experimental results demonstrate the method's effectiveness compared to existing segmentation algorithms.
    • The approach exhibits inherent parallelism, allowing for improved efficiency.

    Conclusions:

    • The developed clustering model offers a flexible and effective solution for image segmentation.
    • The cellular coevolutionary optimization approach is well-suited for this clustering task.
    • The method's ability to handle unknown cluster numbers and its parallel nature are significant advantages.