A Learning-Based Approach for Perceptual Models of Preference

Junhui Mei; Xinyi Le; Xiaoting Zhang; Charlie C.L. Wang

doi:10.1007/978-3-030-22796-8_35

A Learning-Based Approach for Perceptual Models of Preference

Junhui Mei, Xinyi Le^*, Xiaoting Zhang, Charlie C.L. Wang

^*Corresponding author for this work

Mechanical and Aerospace Engineering

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

Abstract

This paper introduces a novel data-driven approach based on subjective constraints and feature learning for training perceptual models of preference. Fuzzy evaluation is applied to describe the subjective opinions from a large set of data collected from user study. Combined with the objective attributes of the training models and the subjective preferences, an optimization method is developed successfully for training and learning perceptual models. Two applications are given in details for the selection of “best” viewpoint of 3D objects and the optimized direction of 3D printing, which verify the effectiveness of our approach. This work also demonstrate a good human-computer interaction practice that draws supporting knowledge from both the machine side and the human side.

Original language	English
Title of host publication	Advances in Neural Networks – ISNN 2019 - 16th International Symposium on Neural Networks, ISNN 2019, Proceedings
Editors	Huchuan Lu, Huajin Tang, Zhanshan Wang
Publisher	Springer-Verlag Italia
Pages	328-339
Number of pages	12
ISBN (Print)	9783030227951
DOIs	https://doi.org/10.1007/978-3-030-22796-8_35
Publication status	Published - 1 Jan 2019
Event	16th International Symposium on Neural Networks, ISNN 2019 - Moscow, Russian Federation Duration: 10 Jul 2019 → 12 Jul 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11554 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th International Symposium on Neural Networks, ISNN 2019
Country/Territory	Russian Federation
City	Moscow
Period	10/07/19 → 12/07/19

Keywords

3D printing direction
Feature learning
Perceptual model
Viewpoint selection

Access to Document

10.1007/978-3-030-22796-8_35

Cite this

Mei, J., Le, X., Zhang, X., & Wang, C. C. L. (2019). A Learning-Based Approach for Perceptual Models of Preference. In H. Lu, H. Tang, & Z. Wang (Eds.), Advances in Neural Networks – ISNN 2019 - 16th International Symposium on Neural Networks, ISNN 2019, Proceedings (pp. 328-339). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11554 LNCS). Springer-Verlag Italia. https://doi.org/10.1007/978-3-030-22796-8_35

Mei, Junhui ; Le, Xinyi ; Zhang, Xiaoting et al. / A Learning-Based Approach for Perceptual Models of Preference. Advances in Neural Networks – ISNN 2019 - 16th International Symposium on Neural Networks, ISNN 2019, Proceedings. editor / Huchuan Lu ; Huajin Tang ; Zhanshan Wang. Springer-Verlag Italia, 2019. pp. 328-339 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "A Learning-Based Approach for Perceptual Models of Preference",

abstract = "This paper introduces a novel data-driven approach based on subjective constraints and feature learning for training perceptual models of preference. Fuzzy evaluation is applied to describe the subjective opinions from a large set of data collected from user study. Combined with the objective attributes of the training models and the subjective preferences, an optimization method is developed successfully for training and learning perceptual models. Two applications are given in details for the selection of “best” viewpoint of 3D objects and the optimized direction of 3D printing, which verify the effectiveness of our approach. This work also demonstrate a good human-computer interaction practice that draws supporting knowledge from both the machine side and the human side.",

keywords = "3D printing direction, Feature learning, Perceptual model, Viewpoint selection",

author = "Junhui Mei and Xinyi Le and Xiaoting Zhang and Wang, {Charlie C.L.}",

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editor = "Huchuan Lu and Huajin Tang and Zhanshan Wang",

booktitle = "Advances in Neural Networks – ISNN 2019 - 16th International Symposium on Neural Networks, ISNN 2019, Proceedings",

address = "Italy",

note = "16th International Symposium on Neural Networks, ISNN 2019 ; Conference date: 10-07-2019 Through 12-07-2019",

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Mei, J, Le, X, Zhang, X & Wang, CCL 2019, A Learning-Based Approach for Perceptual Models of Preference. in H Lu, H Tang & Z Wang (eds), Advances in Neural Networks – ISNN 2019 - 16th International Symposium on Neural Networks, ISNN 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11554 LNCS, Springer-Verlag Italia, pp. 328-339, 16th International Symposium on Neural Networks, ISNN 2019, Moscow, Russian Federation, 10/07/19. https://doi.org/10.1007/978-3-030-22796-8_35

A Learning-Based Approach for Perceptual Models of Preference. / Mei, Junhui; Le, Xinyi; Zhang, Xiaoting et al.
Advances in Neural Networks – ISNN 2019 - 16th International Symposium on Neural Networks, ISNN 2019, Proceedings. ed. / Huchuan Lu; Huajin Tang; Zhanshan Wang. Springer-Verlag Italia, 2019. p. 328-339 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11554 LNCS).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

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AB - This paper introduces a novel data-driven approach based on subjective constraints and feature learning for training perceptual models of preference. Fuzzy evaluation is applied to describe the subjective opinions from a large set of data collected from user study. Combined with the objective attributes of the training models and the subjective preferences, an optimization method is developed successfully for training and learning perceptual models. Two applications are given in details for the selection of “best” viewpoint of 3D objects and the optimized direction of 3D printing, which verify the effectiveness of our approach. This work also demonstrate a good human-computer interaction practice that draws supporting knowledge from both the machine side and the human side.

KW - 3D printing direction

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KW - Perceptual model

KW - Viewpoint selection

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Mei J, Le X, Zhang X, Wang CCL. A Learning-Based Approach for Perceptual Models of Preference. In Lu H, Tang H, Wang Z, editors, Advances in Neural Networks – ISNN 2019 - 16th International Symposium on Neural Networks, ISNN 2019, Proceedings. Springer-Verlag Italia. 2019. p. 328-339. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-22796-8_35

A Learning-Based Approach for Perceptual Models of Preference

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