Machine vision benefits from human contextual expectations.

ABSTRACT: Scene context is known to facilitate object recognition in both machines and humans, suggesting that the underlying representations may be similar. Alternatively, they may be qualitatively different since the training experience of machines and humans are strikingly different. Machines are typically trained on images containing objects and their context, whereas humans frequently experience scenes without objects (such as highways without cars). If these context representations are indeed different, machine vision algorithms will be improved on augmenting them with human context representations, provided these expectations can be measured and are systematic. Here, we developed a paradigm to measure human contextual expectations. We asked human subjects to indicate the scale, location and l

SUBMITTER: Katti H 

PROVIDER: S-EPMC6375915 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature