Labeling the Features Not the Samples: Efficient Video Classification with Minimal Supervision
Venue
AAAI (2016)
Publication Year
2016
Authors
Marius Leordeanu, Alexandra Radu, Shumeet Baluja, Rahul Sukthankar
BibTeX
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 and 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.
