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A Learning-Based Approach for Perceptual Models of Preference

  • Junhui Mei
  • Xinyi LeEmail author
  • Xiaoting Zhang
  • Charlie C. L. Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11554)

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

Perceptual model Feature learning Viewpoint selection 3D printing direction 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Junhui Mei
    • 1
  • Xinyi Le
    • 1
    Email author
  • Xiaoting Zhang
    • 2
  • Charlie C. L. Wang
    • 3
  1. 1.Shanghai Jiao Tong UniversityShanghaiChina
  2. 2.Boston UniversityBostonUSA
  3. 3.The Chinese University of Hong KongShatinHong Kong

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