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Related Experiment Video

Updated: Nov 6, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Home Textile Pattern Emotion Labeling Using Deep Multi-View Feature Learning.

Juan Yang1, Yuanpeng Zhang2

  • 1School of Textile and Clothing, Nantong University, Nantong, China.

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|May 6, 2021
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Summary
This summary is machine-generated.

This study introduces an AI framework for labeling home textile pattern emotions using shape, texture, and deep features. The method enhances pattern retrieval for designers and consumers.

Keywords:
deep learningemotion labelingfeature selectionhome textile patternmulti-view learning

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Textile Design

Background:

  • Home textile patterns evoke distinct emotions, impacting user experience and design retrieval.
  • Semantic retrieval of textile patterns can aid designers and consumers in finding suitable designs.

Purpose of the Study:

  • To develop an artificial intelligence-based framework for emotion labeling of home textile patterns.
  • To improve the semantic retrieval of textile patterns by incorporating emotional evaluation.

Main Methods:

  • A three-stage framework was developed for pattern emotion labeling.
  • Aesthetic features (shape, texture, salient region) were extracted.
  • A convolutional neural network (CNN) was used for deep feature extraction.
  • A multi-view classifier was designed for automatic weight learning and labeling.

Main Results:

  • The proposed three-stage framework demonstrated promising performance in home textile pattern labeling.
  • The integration of aesthetic and deep features improved labeling accuracy.

Conclusions:

  • The developed AI framework effectively labels emotions in home textile patterns.
  • This approach enhances the usability of textile pattern databases for both design and selection processes.