Active Learning with n-ary Queries for Image Recognition

Active learning algorithms automatically identify the salient and informative samples from large amounts of unlabeled data and tremendously reduce human annotation effort in inducing a machine learning model. In a multi-class classification problem, however, the human oracle has to provide the precise category label of each unlabeled sample to be annotated. In an application with a significantly large (and possibly unknown) number of classes (such as object recognition), providing the exact class label may be time consuming and error prone. In this paper, we propose a novel active learning framework where the annotator merely needs to identify which of the selected n categories a given unlabeled sample belongs to (where n is much smaller than the actual number of classes). We pose the active sample selection as an NP-hard integer quadratic programming problem and exploit the Iterative Truncated Power algorithm to derive an efficient solution. To the best of our knowledge, this is the first research effort to propose a generic n-ary query framework for active sample selection. Our extensive empirical results on six challenging vision datasets (from four different application domains and varied number of classes ranging from 10 to 369) corroborate the potential of the framework in further reducing human annotation effort in real-world active learning applications.