Towards Scalable Dataset Construction: An Active Learning Approach

Authors:
Brendan Collins;Jia Deng;Kai Li;Li Fei-Fei
Affiliations:
Department of Computer Science, Princeton University, New Jersey, U.S.A.;Department of Computer Science, Princeton University, New Jersey, U.S.A.;Department of Computer Science, Princeton University, New Jersey, U.S.A.;Department of Computer Science, Princeton University, New Jersey, U.S.A.
Venue:
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Year:
2008

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Abstract

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.