Incremental transfer learning for object recognition in streaming video

We present a new incremental learning framework for realtime object recognition in video streams. ImageNet is used to bootstrap a set of one-vs-all incrementally trainable SVMs which are updated by user annotation events during streaming. We adopt an inductive transfer learning (ITL) approach to warp the video feature space to the ImageNet feature space, so enabling the incremental updates. Uniquely, the transformation used for the ITL warp is also learned incrementally using the same update events. We demonstrate a semi-automated video logging (SAVL) system using our incrementally learned ITL approach and show this to outperform existing SAVL which uses non-incremental transfer learning.