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Improving Crowdsourcing-Based Image Classification Through Expanded Input Elicitation and Machine Learning.

Romena Yasmin1, Md Mahmudulla Hassan2, Joshua T Grassel1

  • 1School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, United States.

Frontiers in Artificial Intelligence
|July 18, 2022
PubMed
Summary
This summary is machine-generated.

Crowdsourcing image classification benefits from combining binary labels with confidence scores for better accuracy, especially with limited data. Augmenting machine learning models with automated classifier outputs can further enhance performance.

Keywords:
crowdsourcinghuman computationimage classificationinput elicitationsmachine learning

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Area of Science:

  • Computer Science
  • Machine Learning
  • Computer Vision

Background:

  • Image classification tasks require accurate labels, often obtained through human annotation.
  • Crowdsourcing offers a scalable approach to data labeling but can suffer from noise and variability.

Purpose of the Study:

  • To investigate how diverse crowdsourcing input elicitation methods improve inferred labels for image classification.
  • To evaluate the effectiveness of various voting and machine learning (ML) methods in leveraging these inputs.
  • To develop a systematic synthetic image generation process for assessing performance on tasks of varying difficulty.

Main Methods:

  • Five input elicitation methods were tested: binary classification, object coordinate, confidence level, perceived majority classification, and task difficulty.
  • Two crowdsourcing studies involving over 300 participants were conducted.
  • A synthetic image generation process using the MPEG-7 Core Experiment CE-Shape-1 Test Set was developed to create images of varying difficulty.

Main Results:

  • Combining crowdsourced binary classification labels with average self-reported confidence values improved ML classifier accuracy, particularly with smaller training datasets.
  • Augmenting ML algorithms with automated classifier outputs can yield higher performance than individual classifiers when a larger annotated dataset is available.
  • Specific modifications to aggregation methods allow prioritization of performance metrics like reduced false-negative rates.

Conclusions:

  • Leveraging multiple crowdsourced data types, especially confidence scores, enhances image classification accuracy.
  • Hybrid approaches combining human and automated intelligence offer superior performance in image classification.
  • Crowdsourcing methodologies can be adapted to optimize for specific performance metrics beyond simple accuracy.