Labeling the Features Not the Samples: Efficient Video Classification with Minimal Supervision

Authors

  • Marius Leordeanu Institute of Mathematics of the Romanian Academy
  • Alexandra Radu Institute of Mathematics of the Romanian Academy
  • Shumeet Baluja Google Research
  • Rahul Sukthankar Google Research

DOI:

https://doi.org/10.1609/aaai.v30i1.10467

Keywords:

feature selection, video classification, semi-supervised learning

Abstract

Feature selection is essential for effective visual recognition. We propose an efficient joint classifier learning and feature selection method that discovers sparse, compact representations of input features from a vast sea of candidates, with an almost unsupervised formulation. Our method requires only the following knowledge, which we call the feature sign - whether or not a particular feature has on average stronger values over positive samples than over negatives. We show how this can be estimated using as few as a single labeled training sample per class. Then, using these feature signs, we extend an initial supervised learning problem into an (almost) unsupervised clustering formulation that can incorporate new data without requiring ground truth labels. Our method works both as a feature selection mechanism and as a fully competitive classifier. It has important properties, low computational cost annd excellent accuracy, especially in difficult cases of very limited training data. We experiment on large-scale recognition in video and show superior speed and performance to established feature selection approaches such as AdaBoost, Lasso, greedy forward-backward selection, and powerful classifiers such as SVM.

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Published

2016-03-05

How to Cite

Leordeanu, M., Radu, A., Baluja, S., & Sukthankar, R. (2016). Labeling the Features Not the Samples: Efficient Video Classification with Minimal Supervision. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.10467