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A Learning-Based Approach for Perceptual Models of Preference

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Advances in Neural Networks – ISNN 2019 (ISNN 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11554))

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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.

The work described in the paper was jointly sponsored by Natural Science Foundation of Shanghai (18ZR1420100) and National Natural Science Foundation of China (61703274). This work was partially supported by NSFC 61628211.

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Author information

Authors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, 200240, China

    Junhui Mei & Xinyi Le

  2. Boston University, Boston, MA, USA

    Xiaoting Zhang

  3. The Chinese University of Hong Kong, Shatin, Hong Kong

    Charlie C. L. Wang

Authors
  1. Junhui Mei
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  2. Xinyi Le
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  3. Xiaoting Zhang
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  4. Charlie C. L. Wang
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Corresponding author

Correspondence to Xinyi Le .

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Huchuan Lu

  2. Sichuan University, Chengdu, China

    Huajin Tang

  3. Northeastern University, Shenyang, China

    Zhanshan Wang

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Mei, J., Le, X., Zhang, X., Wang, C.C.L. (2019). A Learning-Based Approach for Perceptual Models of Preference. In: Lu, H., Tang, H., Wang, Z. (eds) Advances in Neural Networks – ISNN 2019. ISNN 2019. Lecture Notes in Computer Science(), vol 11554. Springer, Cham. https://doi.org/10.1007/978-3-030-22796-8_35

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  • DOI: https://doi.org/10.1007/978-3-030-22796-8_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-22795-1

  • Online ISBN: 978-3-030-22796-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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