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In general, a schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
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A Real-world What-Where-When Memory Test
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Defining Image Memorability Using the Visual Memory Schema.

Erdem Akagunduz, Adrian G Bors, Karla K Evans

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |May 7, 2019
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    Summary
    This summary is machine-generated.

    This study introduces the Visual Memory Schema (VMS) to predict image memorability by analyzing shared human annotations. VMS enhances understanding and prediction of how memorable images are, independent of individual observers.

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    Area of Science:

    • Cognitive Science
    • Computer Vision
    • Psychology

    Background:

    • Image memorability is traditionally observer-dependent, but recent work treats it as an intrinsic property.
    • Existing models for image memorability prediction have limitations.
    • Understanding shared human perception of image memorability is crucial for improving predictive models.

    Purpose of the Study:

    • To enhance the understanding and prediction of image memorability.
    • To introduce and operationalize the concept of Visual Memory Schema (VMS).
    • To investigate the relationship between VMS, human annotations, eye fixations, and saliency.

    Main Methods:

    • Human observers identified memorable image regions during an episodic memory test.
    • Statistical assessment of Visual Memory Schema (VMS) consistency across observers.
    • Analysis of VMS associations with eye fixations and saliency maps.
    • Adaptation of deep learning architectures for memorable region reconstruction and prediction, including transfer learning.

    Main Results:

    • Consistent patterns in Visual Memory Schemas (VMS) were observed across observers for both correctly and incorrectly recognized images.
    • Associations between VMS, eye fixations, and saliency were analyzed.
    • Deep learning models demonstrated potential for reconstructing and predicting memorable image regions.

    Conclusions:

    • The Visual Memory Schema (VMS) offers a novel framework for understanding shared human perception in image memorability.
    • Cumulative human annotations, operationalized through VMS, improve image memorability prediction.
    • Deep learning approaches show promise in leveraging VMS for predicting memorable image regions.