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

Associative Learning01:27

Associative Learning

2.1K
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
2.1K
Aggregates Classification01:29

Aggregates Classification

1.0K
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...
1.0K
Classification of Systems-II01:31

Classification of Systems-II

651
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,
651
Force Classification01:22

Force Classification

2.8K
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,...
2.8K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

544
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 of...
544
Classification of Systems-I01:26

Classification of Systems-I

742
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:
742

You might also read

Related Articles

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

Sort by
Same author

Effects of symbiotic bacteria on the parasitism efficacy of <i>Aphidius gifuensis</i> against <i>Myzus persicae</i>.

Frontiers in microbiology·2026
Same author

Early <i>BCR::ABL1</i> Reduction as a Predictor of Deep Molecular Response in Pediatric Chronic-Phase Chronic Myeloid Leukemia.

Cancers·2025
Same author

STWM: Sliding Time Window Method Driven by Enzymes for Image Information Access.

ACS applied materials & interfaces·2025
Same author

Engineering a dual-loop molecular circuit with buffering capability to solve molecular information tasks.

Nanoscale·2024
Same author

Machine learning provides insights for spatially explicit pest management strategies by integrating information on population connectivity and habitat use in a key agricultural pest.

Pest management science·2024
Same author

The immune response mechanism of Nilaparvata lugens against a combined infection of rice ragged stunt virus and Metarhizium anisopliae.

Pest management science·2023
Same journal

SinColor: Uncertainty-Guided Single-Step Diffusion for Image Colorization.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Through the Looking Glass: A Dual Perspective on Weakly-Supervised Few-Shot Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Mask-guided Asymmetric Contrastive and Semantic Alignment for Unsupervised Person Re-Identification.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Hyperbolic Cycle Alignment for Infrared-Visible Image Fusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Learning Gaze Synthesizer via 3D-eye Controlled Diffusion and Cross-domain Feature Alignment.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Underlying Semantic Diffusion for Effective and Efficient In-Context Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles

Related Experiment Video

Updated: May 1, 2026

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

8.6K

Multilabel image classification via high-order label correlation driven active learning.

Bang Zhang, Yang Wang, Fang Chen

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |April 12, 2014
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel active learning approach (HoAL) for multilabel image classification, reducing annotation needs by intelligently selecting informative example-label pairs. HoAL leverages high-order label correlations for efficient and accurate classifier training with less human effort.

    Related Experiment Videos

    Last Updated: May 1, 2026

    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

    8.6K

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Supervised machine learning excels in multilabel image classification but requires extensive human annotation for training data.
    • The high cost and effort of acquiring annotated images limit the scalability of supervised learning methods.
    • Existing active learning methods often focus on binary classification or simpler label dependencies.

    Purpose of the Study:

    • To develop an efficient active learning strategy for multilabel image classification that minimizes annotation effort.
    • To address the challenges of example-level selection granularity and complex label dependencies in multilabel settings.
    • To propose a high-order label correlation driven active learning (HoAL) approach for improved classifier accuracy with reduced labeling costs.

    Main Methods:

    • The proposed HoAL approach iteratively selects informative example-label pairs for annotation.
    • It considers fine-grained selection at the example-label pair level, unlike traditional methods.
    • HoAL incorporates both pairwise and high-order label correlations, along with an efficient mining method for discovering informative correlations.

    Main Results:

    • The HoAL approach demonstrated effectiveness in reducing annotation efforts for multilabel image classification tasks.
    • Empirical results on public datasets validate the method's ability to train accurate classifiers with less human input.
    • The incorporation of high-order label correlations proved beneficial for efficient learning.

    Conclusions:

    • The HoAL approach offers a scalable solution for multilabel image classification by significantly reducing the need for human annotation.
    • Leveraging high-order label correlations is a promising direction for enhancing active learning in complex classification scenarios.
    • This method enables the development of accurate classifiers with reduced annotation costs, facilitating broader applications.