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

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

This work investigates how different forms of input elicitation obtained from crowdsourcing can be utilized to improve the quality of inferred labels for image classification tasks, where an image must be labeled as either positive or negative depending on the presence/absence of a specified object....

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Bibliographic Details
Main Authors: Romena Yasmin, Md Mahmudulla Hassan, Joshua T. Grassel, Harika Bhogaraju, Adolfo R. Escobedo, Olac Fuentes
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-06-01
Series:Frontiers in Artificial Intelligence
Subjects:
machine learning
input elicitations
crowdsourcing
human computation
image classification
Online Access:https://www.frontiersin.org/articles/10.3389/frai.2022.848056/full

Internet

https://www.frontiersin.org/articles/10.3389/frai.2022.848056/full

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