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Online semi-supervised perception: Real-time learning without explicit feedback

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4 Author(s)
Kveton, B. ; Intel Labs., Santa Clara, CA, USA ; Philipose, M. ; Valko, M. ; Ling Huang

This paper proposes an algorithm for real-time learning without explicit feedback. The algorithm combines the ideas of semi-supervised learning on graphs and online learning. In particular, it iteratively builds a graphical representation of its world and updates it with observed examples. Labeled examples constitute the initial bias of the algorithm and are provided offline, and a stream of unlabeled examples is collected online to update this bias. We motivate the algorithm, discuss how to implement it efficiently, prove a regret bound on the quality of its solutions, and apply it to the problem of real-time face recognition. Our recognizer runs in real time, and achieves superior precision and recall on 3 challenging video datasets.

Published in:

Computer Vision and Pattern Recognition Workshops (CVPRW), 2010 IEEE Computer Society Conference on

Date of Conference:

13-18 June 2010