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

Factorial Design

Factorial Design

Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...

You might also read

Related Articles

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

Sort by

Same author

A Transformer-Based Variational Autoencoder for Training Data Generation in Spindle Motor Vibration-Based Anomaly Detection.

Sensors (Basel, Switzerland)·2026

Same author

Precision Biomarker Identification in Gynecological Cancers Using Coexpression Networks and Attention-Based LSTM in Healthcare 4.0.

Diagnostics (Basel, Switzerland)·2026

Same author

Pemphigus-Associated Desmoglein-Specific IgG1 and IgG4 Have a Dominant Agalactosylated Glycan Modification.

The Journal of investigative dermatology·2024

Same author

Nanoparticle Exsolution on Perovskite Oxides: Insights into Mechanism, Characteristics and Novel Strategies.

Nano-micro letters·2023

Same author

The Advent of Domain Adaptation into Artificial Intelligence for Gastrointestinal Endoscopy and Medical Imaging.

Diagnostics (Basel, Switzerland)·2023

Same author

Video Analysis of Small Bowel Capsule Endoscopy Using a Transformer Network.

Diagnostics (Basel, Switzerland)·2023

Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026

Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026

Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026

Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026

Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026

Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 27, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Efficient Image Retrieval Using Hierarchical K-Means Clustering.

Dayoung Park¹, Youngbae Hwang¹

¹Department of Control and Robot Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea.

Sensors (Basel, Switzerland)

|April 27, 2024

Summary

This summary is machine-generated.

This study introduces hierarchical K-means clustering for faster content-based image retrieval (CBIR). The method significantly speeds up image searches by organizing image descriptors efficiently, improving performance on various models.

Keywords:

CBIR efficiency hierarchical clustering image retrieval tree search

More Related Videos

Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes

Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes

Published on: March 20, 2018

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

Related Experiment Videos

Last Updated: Jun 27, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes

Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes

Published on: March 20, 2018

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

Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Content-based image retrieval (CBIR) aims to find similar images using their content.
Current methods often encode images as global descriptors for similarity comparison.
Efficiently organizing these descriptors is crucial for optimizing retrieval speed.

Purpose of the Study:

To propose an optimized image retrieval method using hierarchical K-means clustering.
To enhance the efficiency of organizing image descriptors within a database.
To improve the speed-accuracy trade-off in image retrieval systems.

Main Methods:

Implementing hierarchical K-means clustering to organize image descriptors.
Computing similarity between query descriptors and descriptors in leaf nodes.
Utilizing three tree search algorithms for adjustable speed-accuracy balance.

Main Results:

Demonstrated significant enhancement in image retrieval speed.
Achieved an 18-times speed improvement on the In-Shop dataset.
Preserved over 99% accuracy while increasing retrieval speed.

Conclusions:

Hierarchical K-means clustering effectively optimizes image descriptor organization for faster CBIR.
The proposed method offers substantial speed gains with minimal accuracy loss.
Validated effectiveness across different models (UNICOM, R-GeM) and datasets.