Multiclass recognition and part localization with humans in the loop
Research output: Contribution to journal › Conference article › Research › peer-review
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Multiclass recognition and part localization with humans in the loop. / Wah, Catherine; Branson, Steve; Perona, Pietro; Belongie, Serge.
In: Proceedings of the IEEE International Conference on Computer Vision, 2011, p. 2524-2531.Research output: Contribution to journal › Conference article › Research › peer-review
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TY - GEN
T1 - Multiclass recognition and part localization with humans in the loop
AU - Wah, Catherine
AU - Branson, Steve
AU - Perona, Pietro
AU - Belongie, Serge
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84856635994&partnerID=8YFLogxK
U2 - 10.1109/ICCV.2011.6126539
DO - 10.1109/ICCV.2011.6126539
M3 - Conference article
AN - SCOPUS:84856635994
SP - 2524
EP - 2531
JO - Proceedings of the IEEE International Conference on Computer Vision
JF - Proceedings of the IEEE International Conference on Computer Vision
T2 - 2011 IEEE International Conference on Computer Vision, ICCV 2011
Y2 - 6 November 2011 through 13 November 2011
ER -
ID: 301830771