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

Search research articles

Related Experiment Videos

Image annotation by multiple-instance learning with discriminative feature mapping and selection.

Richang Hong, Meng Wang, Yue Gao

IEEE Transactions on Cybernetics

|June 26, 2013

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same author

Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective.

IEEE transactions on pattern analysis and machine intelligence·2026

Same author

Holistic Invariant Retracing for Distortion-Resilient Multi-Modal Learning in Spatial Transcriptomics.

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

Same author

Demonstration of efficient predictive surrogates for large-scale quantum processors.

Nature communications·2026

Same author

A DeepSeek-powered AI system for automated chest radiograph interpretation in clinical practice.

Nature communications·2026

Same author

NoisePO: Efficient Semantic Noise Generation and Ranking for Diffusion-Based Text-to-Image Synthesis.

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

Same author

Stability and Generalization for Distributed SGDA.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

A New Human-Likeness and Comfort Index for Robot Movements Along Prescribed Paths.

IEEE transactions on cybernetics·2026

Same journal

Robust Semiglobal and Global Stabilization for Nonlinear Normal Form Systems by Time-Varying Feedback.

IEEE transactions on cybernetics·2026

Same journal

Adaptive Global Asymptotic Output Stabilization of Uncertain Nonlinear Systems Under Dynamic State/Input Quantization.

IEEE transactions on cybernetics·2026

Same journal

Accelerated Distributed Gradient Tracking for Constrained Aggregative Optimization Over Time-Varying Digraphs.

IEEE transactions on cybernetics·2026

Same journal

Small-Gain-Based Plug-and-Play Distributed Control Framework for DC Microgrids With Decentralized Reconfiguration.

IEEE transactions on cybernetics·2026

Same journal

Prescribed-Time Impulsive Control of High-Order Integrator Systems.

IEEE transactions on cybernetics·2026

See all related articles

This study introduces a novel multiple-instance learning (MIL) method for image annotation. The approach enhances feature mapping and selection, improving accuracy by addressing noise and discriminative ability in visual data.

Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Multiple-instance learning (MIL) is used for image annotation by analyzing region-level information.
Existing MIL methods can be converted to single-instance learning via feature mapping.
Current feature mapping techniques often neglect feature discriminability and introduce noise.

Purpose of the Study:

To propose a novel MIL method incorporating discriminative feature mapping and feature selection.
To address limitations in existing feature mapping approaches for MIL.
To enhance the accuracy and robustness of image annotation using MIL.

Main Methods:

Developed a discriminative feature mapping technique for MIL.
Implemented a feature selection mechanism to identify effective low-level features.

Related Experiment Videos

Explored both positive and negative concept correlations within the MIL framework.

Main Results:

The proposed method effectively handles discriminative ability and noise in feature mapping.
The approach successfully selects relevant features from a large set of low-level features.
Experimental results show superior performance compared to existing MIL methods.

Conclusions:

The novel MIL method with discriminative feature mapping and selection is effective for image annotation.
The approach improves upon existing methods by better managing feature quality.
This work offers a robust solution for concept correlation exploration in MIL settings.