Multiclass recognition and part localization with humans in the loop

Authors:
Catherine Wah;Steve Branson;Pietro Perona;Serge Belongie
Affiliations:
Department of Computer Science and Engineering, University of California, San Diego, USA;Department of Computer Science and Engineering, University of California, San Diego, USA;Department of Electrical Engineering, California Institute of Technology, USA;Department of Electrical Engineering, California Institute of Technology, USA
Venue:
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Year:
2011

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Attributes for classifier feedback

ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III

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Abstract

We propose a visual recognition system that is designed for fine-grained visual categorization. The system is composed of a machine and a human user. The user, who is unable to carry out the recognition task by himself, is interactively asked to provide two heterogeneous forms of information: clicking on object parts and answering binary questions. The machine intelligently selects the most informative question to pose to the user in order to identify the object's class as quickly as possible. By leveraging computer vision and analyzing the user responses, the overall amount of human effort required, measured in seconds, is minimized. We demonstrate promising results on a challenging dataset of uncropped images, achieving a significant average reduction in human effort over previous methods.