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MetaMP: Metalearning-Based Multipatch Image Aesthetics Assessment.

Jiachen Yang, Yanshuang Zhou, Yang Zhao

    IEEE Transactions on Cybernetics
    |May 17, 2022
    PubMed
    Summary

    This study introduces a novel metalearning-based multipatch (MetaMP) method for image aesthetics assessment (IAA). The MetaMP approach quickly adapts to diverse themes, improving accuracy and guiding network initialization for better aesthetic evaluations.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Image aesthetics assessment (IAA) is inherently subjective and complex, with significant variations across different themes.
    • Direct fine-tuning of pretrained networks often struggles with rapid adaptation to diverse thematic tasks in IAA.
    • Existing methods may not effectively integrate fine details with the overall impression for accurate aesthetic evaluation.

    Purpose of the Study:

    • To introduce a novel metalearning-based multipatch (MetaMP) method for efficient and accurate image aesthetics assessment.
    • To enable rapid adaptation of IAA models to various thematic tasks.
    • To improve the integration of local image details with global aesthetic perception.

    Main Methods:

    • Developed a metalearning-based approach for training IAA networks to achieve content-oriented aesthetic expression.
    • Designed a complete-information patch selection scheme for comprehensive image analysis.
    • Implemented a multipatch (MP) network architecture to harmonize fine details with the overall aesthetic impression.

    Main Results:

    • The proposed MetaMP method demonstrated superior performance compared to state-of-the-art models on the Aesthetic Visual Analysis (AVA) benchmark datasets.
    • Experimental results validated the effectiveness of the metalearning training strategy for IAA.
    • The method significantly improved assessment accuracy and provided valuable guidance for network initialization.

    Conclusions:

    • The metalearning-based multipatch (MetaMP) method offers a robust solution for adaptive and accurate image aesthetics assessment across diverse themes.
    • The developed metalearning training model enhances IAA performance and offers practical insights for initializing deep learning models in this domain.
    • This approach effectively addresses the challenges of subjectivity and thematic variation in image aesthetic evaluation.