Semi-automated video logging by incremental and transfer learning

We describe a semi-automatic video logging system, capable of annotating frames with semantic metadata describing the objects present. The system learns by visual examples provided interactively by the logging operator, which are learned incrementally to provide increased automation over time. Transfer learning is initially used to bootstrap the system using relevant visual examples from ImageNet. We adapt the hard-assignment Bag of Word strategy for object recognition to our interactive use context, showing transfer learning to significantly reduce the degree of interaction required.