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Poster
Understanding the Intrinsic Memorability of Images
Phillip Isola · Devi Parikh · Antonio Torralba · Aude Oliva

Wed Dec 14 08:45 AM -- 02:59 PM (PST) @
in Poster Session »

Artists, advertisers, and photographers are routinely presented with the task of creating an image that a viewer will remember. While it may seem like image memorability is purely subjective, recent work shows that it is not an inexplicable phenomenon: variation in memorability of images is consistent across subjects, suggesting that some images are intrinsically more memorable than others, independent of a subjects' contexts and biases. In this paper, we used the publicly available memorability dataset of Isola et al., and augmented the object and scene annotations with interpretable spatial, content, and aesthetic image properties. We used a feature-selection scheme with desirable explaining-away properties to determine a compact set of attributes that characterizes the memorability of any individual image. We find that images of enclosed spaces containing people with visible faces are memorable, while images of vistas and peaceful scenes are not. Contrary to popular belief, unusual or aesthetically pleasing scenes do not tend to be highly memorable. This work represents one of the first attempts at understanding intrinsic image memorability, and opens a new domain of investigation at the interface between human cognition and computer vision.

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Author Information

Phillip Isola (Massachusetts Institute of Technology)
Devi Parikh (FAIR (Meta AI) / Georgia Tech)
Antonio Torralba (Massachusetts Institute of Technology)
Aude Oliva (MIT)

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