Neural network models for product image design

Yang Cheng Lin, Hsin Hsi Lai, Chung Hsing Yeh

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

This paper develops four neural network models to help product developers work out a combination of product form elements for best matching a given product image. By applying four most widely used rules for determining the number of hidden neurons, these four models can be used to determine the value of the product image for a given combination of product form elements. An experimental study on mobile phones is conducted to evaluate the performance of these four models. The result of this study shows that there is no best rule for building the models and the performance of these models does not differ significantly. Although the mobile phones are chosen as the object of the experimental study, the approach presented is applicable to other products where a combination of form or other design elements is to be determined for matching a desirable product image.

Original languageEnglish
Pages (from-to)581-588
Number of pages8
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3215
Publication statusPublished - 2004

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Neural network models for product image design'. Together they form a unique fingerprint.

Cite this