The Community for Technology Leaders
2013 IEEE Conference on Computer Vision and Pattern Recognition (2013)
Portland, OR, USA USA
June 23, 2013 to June 28, 2013
ISSN: 1063-6919
pp: 580-587
ABSTRACT
Fine-grained recognition concerns categorization at sub-ordinate levels, where the distinction between object classes is highly local. Compared to basic level recognition, fine-grained categorization can be more challenging as there are in general less data and fewer discriminative features. This necessitates the use of stronger prior for feature selection. In this work, we include humans in the loop to help computers select discriminative features. We introduce a novel online game called "Bubbles" that reveals discriminative features humans use. The player's goal is to identify the category of a heavily blurred image. During the game, the player can choose to reveal full details of circular regions ("bubbles"), with a certain penalty. With proper setup the game generates discriminative bubbles with assured quality. We next propose the "Bubble Bank" algorithm that uses the human selected bubbles to improve machine recognition performance. Experiments demonstrate that our approach yields large improvements over the previous state of the art on challenging benchmarks.
INDEX TERMS
CITATION
Li Fei-Fei, Jonathan Krause, Jia Deng, "Fine-Grained Crowdsourcing for Fine-Grained Recognition", 2013 IEEE Conference on Computer Vision and Pattern Recognition, vol. 00, no. , pp. 580-587, 2013, doi:10.1109/CVPR.2013.81
92 ms
(Ver 3.3 (11022016))