An interactive learning system for large-scale multimedia analytics

Abstract

Analyzing multimedia collections in order to gain insight is a common desire amongst industry and society. Recent research has shown that while machines are getting better at analyzing multimedia data, they still lack the understanding and flexibility of humans. A central conjecture in Multimedia Analytics is that interactive learning is a key method to bridge the gap between human and machine. We investigate the requirements and design of the Exquisitor system, a very large-scale interactive learning system that aims to verify the validity of this conjecture. We describe the architecture and initial scalability results for Exquisitor, and propose research directions related to both performance and result quality.