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

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

107
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
107
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

264
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
264
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

12.2K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
12.2K
Classification of Signals01:30

Classification of Signals

472
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...
472
Classification of Systems-II01:31

Classification of Systems-II

149
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,
149
Correlations02:20

Correlations

32.8K
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
32.8K

You might also read

Related Articles

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

Sort by
Same author

[Biodegradation of o-chlorophenol by photosynthetic bacteria under co-metabolism].

Ying yong sheng tai xue bao = The journal of applied ecology·2011
Same author

Nuclear factor high-mobility group box1 mediating the activation of Toll-like receptor 4 signaling in hepatocytes in the early stage of nonalcoholic fatty liver disease in mice.

Hepatology (Baltimore, Md.)·2011
Same author

"Chemical blowing" of thin-walled bubbles: high-throughput fabrication of large-area, few-layered BN and C(x) -BN nanosheets.

Advanced materials (Deerfield Beach, Fla.)·2011
Same author

Gemcitabine Cytotoxicity: Interaction of Efflux and Deamination.

Journal of drug metabolism & toxicology·2011
Same author

Tanghinigenin from seeds of Cerbera manghas L. induces apoptosis in human promyelocytic leukemia HL-60 cells.

Environmental toxicology and pharmacology·2011
Same author

The pathogenic and vaccine strains of equine infectious anemia virus differentially induce cytokine and chemokine expression and apoptosis in macrophages.

Virus research·2011

Related Experiment Video

Updated: Jul 9, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

559

Semantic and Correlation Disentangled Graph Convolutions for Multilabel Image Recognition.

Shaofei Cai, Liang Li, Xinzhe Han

    IEEE Transactions on Neural Networks and Learning Systems
    |November 30, 2023
    PubMed
    Summary

    This study introduces a new method for multilabel image recognition (MLR) that effectively uses image-specific label correlations. The proposed approach improves accuracy by focusing on unique relationships within each image, outperforming existing methods on benchmark datasets.

    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

    9.0K
    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    2.8K

    Related Experiment Videos

    Last Updated: Jul 9, 2025

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    559
    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

    9.0K
    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    2.8K

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Multilabel image recognition (MLR) faces challenges with object occlusion and small object sizes.
    • Existing MLR methods often rely on global label correlations, neglecting image-specific relationships.
    • This limits the ability to accurately predict labels for complex scenes.

    Purpose of the Study:

    • To propose a novel method for MLR that leverages image-specific label correlations.
    • To address the limitations of global correlation-based approaches in MLR.
    • To improve the accuracy of MLR, especially for challenging cases with occluded or small objects.

    Main Methods:

    • Introduced the Semantic and Correlation Disentangled Graph Convolution (SCD-GC) method.
    • Developed a semantic disentangling module to extract categorywise semantic features as graph nodes.
    • Created a correlation disentangling module to extract image-specific label correlations as graph edges, forming an image-specific graph for graph convolutions.

    Main Results:

    • The SCD-GC method effectively disentangles dominant label correlations within an image.
    • Graph convolutions on the image-specific graph enhance the mining of labels with weak visual representations.
    • Superior results were achieved on benchmark datasets including MS-COCO, PASCAL-VOC, NUS-WIDE, and VG-500.

    Conclusions:

    • The proposed SCD-GC method significantly advances multilabel image recognition by utilizing image-specific label correlations.
    • This approach offers a more effective way to handle complex scenes and challenging object characteristics in MLR.
    • The method demonstrates strong performance and potential for real-world applications.