BOLD5000, a public fMRI dataset while viewing 5000 visual images

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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 include 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, enables 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.

https://bold5000.github.io

视觉科学,尤其是机器视觉,已经因为大规模图像数据集和统计学习方法地提出而带来革命性的变化。但是目前为止,对于人类视觉感知的神经图像研究依然依赖着少量的数据(常常是100张图片左右),这是因为实验过程受到时间的限制。为了将统计学习的方法介绍到神经科学领域,数据量必须有一个大的提升。我们提出了BOLD5000,一个人类fMRI的研究,它包括接近5000张不同的描述真实场景的图像。相对于之前的fMRI数据集,BOLD5000还更加注重图像的多样性,很多的图像与标准图像数据集有交集,例如SUN,COCO等。除了注重规模和多样性,BOLD5000还结合了慢速事件相关的fMRI设计,这使得在丰富视觉特征和种类以及语义上研究神经表达成为可能。目前,BOLD5000使得我们与Marr的梦想变得更近了:将生物和计算机世界放在一起研究。

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