Title: BOLD5000: A public fMRI dataset of 5000 images,
Abstract: Vision science, particularly machine vision, has been revolutionized by
introducing large-scale image datasets and statistical learning approaches.
Yet, human neuroimaging studies of visual perception still rely on small
numbers of images (around 100) due to time-constrained experimental procedures.
To apply statistical learning approaches that integrate neuroscience, the
number of images used in neuroimaging must be significantly increased. We
present BOLD5000, a human functional MRI (fMRI) study that includes almost
5,000 distinct images depicting real-world scenes. Beyond dramatically
increasing image dataset size relative to prior fMRI studies, BOLD5000 also
accounts for image diversity, overlapping with standard computer vision
datasets by incorporating images from the Scene UNderstanding (SUN), Common
Objects in Context (COCO), and ImageNet datasets. The scale and diversity of
these image datasets, combined with a slow event-related fMRI design, enable
fine-grained exploration into the neural representation of a wide range of
visual features, categories, and semantics. Concurrently, BOLD5000 brings us
closer to realizing Marr's dream of a singular vision science - the intertwined
study of biological and computer vision.