Abstract

As computer vision research considers more ob ject categories and greater variation within ob ject categories, it is clear that larger and more exhaustive datasets are necessary. However, the process of collect- ing such datasets is laborious and monotonous. We consider the setting in which many images have been automatically collected for a visual category (typically by automatic internet search), and we must separate relevant images from noise. We present a discriminative learning process which employs active, online learning to quickly classify many images with minimal user input. The principle advantage of this work over pre- vious endeavors is its scalability. We demonstrate precision which is often superior to the state-of-the-art, with scalability which exceeds previous work.