Editorial: Special Issue on Active and Interactive Methods in Computer Vision

Abstract

While traditional computer vision research aims to replace humans in visual analysis tasks, in many emerging applications there is a need for vision algorithms that instead assist humans or cooperate with them to accomplish recognition and learning tasks. Interactive vision methods allow a human-in-the-loop to inject high-level expertise, while the system carries out low-level processing and/or leverages its own sophisticated statistical models, thereby improving with each iteration. Meanwhile, active learning approaches have the potential to limit the human involvement in such systems to only where it is most crucial. For both types of techniques, recent developments in crowdsourcing, large-scale datasets, and social networks have led to new opportunities and technical challenges. This special issue provides a snapshot of a number of interesting developments in the last few years for active and interactive vision methods. The topics solicited included: methods to actively minimize human effort, human-in-the-loop interactive systems, crowdsourcing issues relevant to vision, and ways to instill human input for machine learning beyond traditional labels. Submissions to the special issue closed in Spring of 2013. All submissions were reviewed by at least three reviewers. Three of these manuscripts (including two on which a guest editor was an author) were handled by other IJCV editors to avoid possible conflicts of interest. We have selected a set of nine papers for the special issue.