Jove
Visualize
Contact Us
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

Related Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

12.5K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
12.5K
Hybrid Zones02:29

Hybrid Zones

20.0K
Hybrid zones are narrow regions where two closely related species interact, mate, and produce hybrids. Relative to either parent species, hybrids may possess distinct phenotypic or genetic differences that impact their survival and reproductive success. The genetic variances introduced by hybridization influence species diversity and speciation processes within the hybrid zone.
20.0K
Shape and Texture of Coarse Aggregate01:25

Shape and Texture of Coarse Aggregate

263
Aggregate shape is classified based on the relative sharpness or roundness of the edges and corners. This classification includes categories like rounded, angular, elongated, and flaky, each with specific characteristics. Rounded aggregates, fully shaped by attrition, are typical of river or seashore gravel, while angular aggregates, such as crushed rock, have well-defined edges. Aggregates that are elongated and flaky are less desirable, as they can reduce the workability and strength of...
263
Classification of Systems-II01:31

Classification of Systems-II

221
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
221
Classification of Signals01:30

Classification of Signals

777
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
777

You might also read

Related Articles

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

Sort by
Same author

Robust 3D Skeletal Joint Fall Detection in Occluded and Rotated Views Using Data Augmentation and Inference-Time Aggregation.

Sensors (Basel, Switzerland)·2025
Same author

Fall Detection of Elderly People Using the Manifold of Positive Semidefinite Matrices.

Journal of imaging·2024
Same author

Oligoclonal Band Straightening Based on Optimized Hierarchical Warping for Multiple Sclerosis Diagnosis.

Sensors (Basel, Switzerland)·2022
Same author

Combination of LBP Bin and Histogram Selections for Color Texture Classification.

Journal of imaging·2021

Related Experiment Video

Updated: Aug 31, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

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

Published on: February 15, 2017

7.0K

Compact Hybrid Multi-Color Space Descriptor Using Clustering-Based Feature Selection for Texture Classification.

Mohamed Alimoussa1,2, Alice Porebski1, Nicolas Vandenbroucke1

  • 1UR 4491, LISIC, Laboratoire d'Informatique Signal et Image de la Côte d'Opale, Univ. Littoral Côte d'Opale, F-62100 Calais, France.

Journal of Imaging
|August 25, 2022
PubMed
Summary

This study introduces a novel color texture representation that combines multiple color spaces and descriptors. This approach significantly enhances classification accuracy, outperforming existing methods, especially with limited data.

Keywords:
chromatic cooccurrence matrixcolor local binary pattern histogramcolor spacescolor texture representationdimensionality reductionfeature selectiontexture classification

More Related Videos

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.2K
A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells
10:37

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

Published on: August 22, 2025

73

Related Experiment Videos

Last Updated: Aug 31, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

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

Published on: February 15, 2017

7.0K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.2K
A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells
10:37

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

Published on: August 22, 2025

73

Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Traditional color texture classification relies on predefined parameters and single color spaces, limiting performance.
  • Selecting appropriate color spaces, descriptors, and settings is a critical challenge impacting classification accuracy.
  • Existing methods often struggle with optimal feature representation for diverse texture patterns.

Purpose of the Study:

  • To develop a robust color texture representation that integrates information from multiple color spaces and descriptor settings.
  • To address the limitations of predetermined configurations in color texture analysis.
  • To create a compact and effective descriptor for improved classification.

Main Methods:

  • Proposed a novel color texture representation by combining features from multiple color spaces and descriptor settings.
  • Implemented a dimensionality reduction scheme using clustering-based sequential feature selection.
  • Developed a compact hybrid multi-color space (CHMCS) descriptor.

Main Results:

  • The CHMCS representation achieved an average accuracy of 94.16% across five benchmark databases.
  • Demonstrated superior performance compared to traditional handcrafted and deep learning methods, exceeding them by over 5%.
  • Showcased particular effectiveness on small datasets.

Conclusions:

  • Combining diverse configurations of color spaces and texture descriptors significantly improves classification accuracy.
  • The proposed CHMCS descriptor offers a powerful and compact solution for color texture representation.
  • This method provides a more adaptable and accurate approach to color texture classification, especially in data-scarce scenarios.