Towards Scalable Dataset Construction: An Active Learning Approach

As computer vision research considers more object categories and greater variation within object categories, it is clear that larger and more exhaustive datasets are necessary. However, the process of collecting 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 previous endeavors is its scalability. We demonstrate precision which is often superior to the state-of-the-art, with scalability which exceeds previous work.

• Type: Conference
• Year: 2008
• Where: ECCV
• Authors: Brendan Collins, Jia Deng, Kai Li, Fei-Fei Li 0002
• Keywords: Automatic Internet Search, Computer Vision, Discriminative Learning Process, ECCV 2008, Exhaustive Datasets, Minimal User Input, Object Categories