TY - GEN
T1 - Impressionist
T2 - ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2016
AU - Yao, Houpu
AU - Ren, Max Yi
N1 - Funding Information:
This work has been supported by the National Science Foundation under Grant No. CMMI-1266184 and the startup funding from Arizona State University. These supports are gratefully acknowledged. We would also like to thank Dr. Hanghang Tang for his generous support, and all crowd participants for their contribution.
Publisher Copyright:
Copyright © 2016 by ASME.
PY - 2016
Y1 - 2016
N2 - Designers often express their intents (e.g., on product functionalities and semantics) through shape features. Therefore, collecting such "salient" features from existing shapes and learning their associations with design intents will enable efficient design of new shapes. However, the acquisition of saliency knowledge from a large shape collection has not been accomplished. This paper investigates a gamification approach to this end. In addition, we propose to validate a derived saliency map by its corresponding shape recognition accuracy through crowdsourcing. This allows a comparison across existing and the proposed saliency acquistion and computation methods. Currentl results show that the proposed method achieves statistically similar recognition accuracy to existing saliency data on a standard shape database, indicating that various saliency maps are equally valid according to the proposed saliency definition. Nonetheless, the saliency data obtained through the proposed game consistently produces reasonable viewpoints across shapes, outperforming existing curvature-based and crowdsourcing approaches. The findings from this study could lead to developments of game mechanisms that are more scalable and cost effective at saliency elicitation than existing paid crowdsourcing approaches.
AB - Designers often express their intents (e.g., on product functionalities and semantics) through shape features. Therefore, collecting such "salient" features from existing shapes and learning their associations with design intents will enable efficient design of new shapes. However, the acquisition of saliency knowledge from a large shape collection has not been accomplished. This paper investigates a gamification approach to this end. In addition, we propose to validate a derived saliency map by its corresponding shape recognition accuracy through crowdsourcing. This allows a comparison across existing and the proposed saliency acquistion and computation methods. Currentl results show that the proposed method achieves statistically similar recognition accuracy to existing saliency data on a standard shape database, indicating that various saliency maps are equally valid according to the proposed saliency definition. Nonetheless, the saliency data obtained through the proposed game consistently produces reasonable viewpoints across shapes, outperforming existing curvature-based and crowdsourcing approaches. The findings from this study could lead to developments of game mechanisms that are more scalable and cost effective at saliency elicitation than existing paid crowdsourcing approaches.
UR - http://www.scopus.com/inward/record.url?scp=85007356682&partnerID=8YFLogxK
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U2 - 10.1115/DETC201660081
DO - 10.1115/DETC201660081
M3 - Conference contribution
AN - SCOPUS:85007356682
T3 - Proceedings of the ASME Design Engineering Technical Conference
BT - 28th International Conference on Design Theory and Methodology
PB - American Society of Mechanical Engineers (ASME)
Y2 - 21 August 2016 through 24 August 2016
ER -