MemCat: A new category-based image set quantified on memorability

Lore Goetschalckx, Johan Wagemans

Abstract:

Images differ in their memorability in consistent ways across observers. What makes
an image memorable is not fully understood to date. Most of the current insight is
in terms of high-level semantic aspects, related to the content. However, research still
shows consistent differences within semantic categories, suggesting a role for factors
at other levels of processing in the visual hierarchy. To aid investigations into this role
as well as contributions to the understanding of image memorability more generally,
we present MemCat. MemCat is a category-based image set, consisting of 10K images
representing five broader, memorability-relevant categories (animal, food, landscape,
sports, and vehicle) and further divided into subcategories (e.g., bear). They were
sampled from existing source image sets that offer bounding box annotations or more
detailed segmentation masks. We collected memorability scores for all 10 K images, each
score based on the responses of on average 99 participants in a repeat-detection memory
task. Replicating previous research, the collected memorability scores show high levels
of consistency across observers. Currently, MemCat is the second largest memorability
image set and the largest offering a category-based structure. MemCat can be used
to study the factors underlying the variability in image memorability, including the
variability within semantic categories. In addition, it offers a new benchmark dataset
for the automatic prediction of memorability scores (e.g., with convolutional neural
networks). Finally, MemCat allows the study of neural and behavioral correlates of
memorability while controlling for semantic category.