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Memorability of an image is a characteristic determined by the humanobservers' ability to remember images they have seen. Yet recent work on imagememorability defines it as an intrinsic property that can be obtainedindependent of the observer. {The current study aims to enhance ourunderstanding and prediction of image memorability, improving upon existingapproaches by incorporating the properties of cumulative human annotations.} Wepropose a new concept called the Visual Memory Schema (VMS) referring to anorganisation of image components human observers share when encoding andrecognising images. The concept of VMS is operationalised by asking humanobservers to define memorable regions of images they were asked to rememberduring an episodic memory test. We then statistically assess the consistency ofVMSs across observers for either correctly or incorrectly recognised images.The associations of the VMSs with eye fixations and saliency are analysedseparately as well. Lastly, we adapt various deep learning architectures forthe reconstruction and prediction of memorable regions in images and analysethe results when using transfer learning at the outputs of differentconvolutional network layers.

Defining Image Memorability using the Visual Memory Schema