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Active Frame Selection for Label Propagation in Videos

  • Sudheendra Vijayanarasimhan
  • Kristen Grauman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7576)

Abstract

Manually segmenting and labeling objects in video sequences is quite tedious, yet such annotations are valuable for learning-based approaches to object and activity recognition. While automatic label propagation can help, existing methods simply propagate annotations from arbitrarily selected frames (e.g., the first one) and so may fail to best leverage the human effort invested. We define an active frame selection problem: select k frames for manual labeling, such that automatic pixel-level label propagation can proceed with minimal expected error. We propose a solution that directly ties a joint frame selection criterion to the predicted errors of a flow-based random field propagation model. It selects the set of k frames that together minimize the total mislabeling risk over the entire sequence. We derive an efficient dynamic programming solution to optimize the criterion. Further, we show how to automatically determine how many total frames k should be labeled in order to minimize the total manual effort spent labeling and correcting propagation errors. We demonstrate our method’s clear advantages over several baselines, saving hours of human effort per video.

Keywords

Dynamic Programming Markov Random Field Foreground Object Label Propagation Video Object 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Sudheendra Vijayanarasimhan
    • 1
  • Kristen Grauman
    • 1
  1. 1.University of Texas at AustinUSA

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