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Updated: Oct 23, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Multi-Label Active Learning Algorithms for Image Classification: Overview and Future Promise.

Jian Wu1, Victor S Sheng2, Jing Zhang3

  • 1Soochow University, China and Human Longevity, Inc., USA.

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|August 23, 2021
PubMed
Summary
This summary is machine-generated.

This survey reviews multi-label active learning for image classification, focusing on sampling strategies and annotation methods. It highlights key challenges and future directions in building effective training datasets.

Keywords:
Additional Key Words and PhrasesImage classificationactive learningannotationmulti-label imagesampling strategy

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Multi-label image classification is crucial for image understanding.
  • Effective training set construction is vital for multi-label classification success.
  • Active learning enhances training sets by selecting informative examples.

Purpose of the Study:

  • To review existing multi-label active learning algorithms for image classification.
  • To categorize algorithms based on sampling and annotation strategies.
  • To investigate challenges and future prospects in the field.

Main Methods:

  • Categorization of algorithms into sampling and annotation groups.
  • Emphasis on designing effective sampling strategies using various information measures.
  • Deep investigation into core aspects: example dimension, label dimension, annotation, and application extension.

Main Results:

  • Existing multi-label active learning algorithms are reviewed and categorized.
  • The importance of informativeness measures in sampling strategies is highlighted.
  • Key challenges and future research directions are identified.

Conclusions:

  • Multi-label active learning is a significant research area for image classification.
  • Effective sampling strategies are central to improving training data.
  • Addressing challenges in dimensions, annotation, and applications is crucial for future progress.