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

Classification of Signals

Classification of Signals

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...

Classification of Leukocytes

Classification of Leukocytes

Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...

Classification of Systems-II

Classification of Systems-II

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,

Force Classification

Force Classification

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,...

How Data are Classified: Categorical Data

How Data are Classified: Categorical Data

A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...

Classification of Systems-I

Classification of Systems-I

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:

You might also read

Related Articles

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

Sort by

Same author

Innovative Integration of Robotic-Assisted Laparoscopic Telesurgery and Quantum Cryptography Communication in Urology: Clinical Application and Initial Experience.

The international journal of medical robotics + computer assisted surgery : MRCAS·2025

Same author

Incremental yield of prenatal exome sequencing in fetuses with skeletal system abnormalities: A systematic review and meta-analysis.

Acta obstetricia et gynecologica Scandinavica·2024

Same author

Highly efficient narrow bandgap Cu(In,Ga)Se<sub>2</sub> solar cells with enhanced open circuit voltage for tandem application.

Nature communications·2024

Same author

Single-cell RNA sequencing analysis reveals the role of TXNDC5 in keloid formation.

CytoJournal·2024

Same author

A pan-orthoebolavirus neutralizing antibody encoded by mRNA effectively prevents virus infection.

Emerging microbes & infections·2024

Same author

Protection against cigarette smoke-induced chronic obstructive pulmonary disease via activation of the SIRT1/FoxO1 axis by targeting microRNA-132.

American journal of translational research·2024

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: Oct 30, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

Exploring Misclassification Information for Fine-Grained Image Classification.

Da-Han Wang^1,2, Wei Zhou^1,2, Jianmin Li^1,2

¹Fujian Key Laboratory of Pattern Recognition and Image Understanding, Xiamen 361024, China.

Sensors (Basel, Switzerland)

|July 2, 2021

Summary

This summary is machine-generated.

This study introduces a new fine-grained image classification method that leverages misclassification information from pre-trained models. This approach improves accuracy by training classifiers on confused classes, enhancing image recognition performance.

Keywords:

confusion information fine-grained image classification misclassification information object categorization

More Related Videos

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

Related Experiment Videos

Last Updated: Oct 30, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Fine-grained image classification is a challenging area of machine learning.
Existing methods often overlook valuable misclassification information.
Improving classification accuracy requires addressing common confusions between similar classes.

Purpose of the Study:

To propose a novel method for fine-grained image classification that utilizes misclassification information.
To enhance classification accuracy by learning from model confusions.
To reduce the impact of irrelevant data by focusing on class-specific confusions.

Main Methods:

A new method, fine-grained image classification by exploring misclassification information (FGMI), is proposed.
Harvesting confusion information from multiple pre-trained models for each class.
Training specialized classifiers using images from classes likely to be misclassified together.
Combining outputs from these confusion classifiers for final image classification.

Main Results:

Experimental validation on public datasets demonstrates the effectiveness of the FGMI method.
The approach successfully improves fine-grained image classification accuracy.
Utilizing misclassification data significantly enhances model performance.

Conclusions:

The proposed FGMI method is effective for fine-grained image classification.
Leveraging misclassification information is a valuable strategy for improving accuracy.
The method offers a promising direction for future research in image recognition.