Fine-grained crowdsourcing for fine-grained recognition

Jia Deng, Jonathan Krause, Li Fei-Fei

Research output: Contribution to journalConference article

177 Scopus citations

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.

Original languageEnglish (US)
Article number6618925
Pages (from-to)580-587
Number of pages8
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
DOIs
StatePublished - 2013
Externally publishedYes
Event26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013 - Portland, OR, United States
Duration: Jun 23 2013Jun 28 2013

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

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