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

Classification of Systems-II01:31

Classification of Systems-II

549
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,
549
Classification of Systems-I01:26

Classification of Systems-I

652
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
652
Aggregates Classification01:29

Aggregates Classification

1.1K
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
1.1K
Classification of Signals01:30

Classification of Signals

1.6K
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...
1.6K
Force Classification01:22

Force Classification

2.6K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
2.6K
Functional Classification of Joints01:09

Functional Classification of Joints

8.9K
Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An...
8.9K

You might also read

Related Articles

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

Sort by
Same author

Functionalized carbon nanotube-assisted dual-mode CRISPR/Cas12a detection of hepatitis C virus via catalytic assembly circuit-driven Y-shaped dsDNA activators.

Biosensors & bioelectronics·2026
Same author

RhoA deficiency in chondrocyte inhibits cartilage fibrosis and ameliorates osteoarthritis progression via SOX4/MMP2 axis.

Journal of orthopaedic translation·2026
Same author

An Electrical Capacitance Tomography Dataset for Image Reconstruction Benchmarking.

Scientific data·2026
Same author

Proactive collaboration via autonomous interaction.

Nature communications·2026
Same author

DynamicTHOR: A Scalable Dataset of Human-Centric Dynamic Scenes for Embodied AI.

Scientific data·2026
Same author

Targeting the PGRN-BMP Lysosomal Axis With NPs@PGRN Reverses Immunometabolic Dysfunction in Chronic Septic Arthritis.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
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

Related Experiment Video

Updated: Mar 23, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.5K

Scene-Level Geographic Image Classification Based on a Covariance Descriptor Using Supervised Collaborative Kernel

Chunwei Yang1,2, Huaping Liu3, Shicheng Wang4

  • 1High-Tech Institute of Xi'an, Xi'an 710025, China. yangchunwei081129@163.com.

Sensors (Basel, Switzerland)
|March 22, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a new supervised collaborative kernel coding method using covariance descriptors (covd) for geographic image classification. The approach effectively enhances scene classification accuracy on high-resolution aerial imagery.

Keywords:
collaborative kernel codingcovariance descriptorscene-level geographic image classification

More Related Videos

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.7K
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

1.7K

Related Experiment Videos

Last Updated: Mar 23, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.5K
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.7K
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

1.7K

Area of Science:

  • Computer Vision
  • Machine Learning
  • Geographic Information Systems

Background:

  • Scene-level geographic image classification is a complex and evolving research area.
  • Existing methods face challenges in accurately categorizing diverse geographic scenes from imagery.

Purpose of the Study:

  • To develop an effective method for scene-level geographic image classification.
  • To improve the accuracy and robustness of image classification using novel feature representation and coding techniques.

Main Methods:

  • Introduced covariance descriptor (covd) for feature extraction.
  • Developed a supervised collaborative kernel coding model to transform covd into Euclidean features.
  • Implemented an iterative optimization framework to solve the proposed model.

Main Results:

  • Demonstrated the effectiveness of the proposed method on a public high-resolution aerial image dataset.
  • Achieved superior performance compared to state-of-the-art scene classification techniques.
  • Validated the method's ability to accurately classify geographic scenes.

Conclusions:

  • The supervised collaborative kernel coding method based on covd is a powerful approach for scene-level geographic image classification.
  • The developed iterative optimization framework effectively solves the proposed model.
  • This research offers a significant advancement in the field of geographic image analysis.