Active Learning For Image Recognition Using A Visualization-Based User Interface

This paper introduces a novel approach for querying samples to be labeled in active learning for image recognition. The user is able to efficiently label images with a visualization for training a classifier. This visualization is achieved by using dimension reduction techniques to create a 2D feature embedding from high-dimensional features. This is made possible by a querying strategy specifically designed for the visualization, seeking optimized bounding-box views for subsequent labeling. The approach is implemented in a web-based prototype. It is compared in-depth to other active learning querying strategies within a user study we conducted with 31 participants on a challenging data set. While using our approach, the participants could train a more accurate classifier than with the other approaches. Additionally, we demonstrate that due to the visualization, the number of labeled samples increases and also the label quality improves.