NDLI: Incremental transfer learning for object recognition in streaming video

Content Provider	IEEE Xplore Digital Library
Author	Kim, Jongdae Collomosse, John
Copyright Year	2014
Description	Author affiliation: Centre for Vision Speech and Signal Processing, University of Surrey, Guildford, United Kingdom (Kim, Jongdae; Collomosse, John)
Abstract	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.
Starting Page	2729
Ending Page	2733
File Size	568842
Page Count	5
File Format	PDF
ISBN	9781479957514
DOI	10.1109/ICIP.2014.7025552
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-27
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Streaming media Training Support vector machines Visualization Manifolds Object recognition Kernel Video Classification Object Recognition Incremental Learning Transfer Learning
Content Type	Text
Resource Type	Article

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