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Related Experiment Video

Updated: Aug 28, 2025

A Simple and Scalable Fabrication Method for Organic Electronic Devices on Textiles

A Simple and Scalable Fabrication Method for Organic Electronic Devices on Textiles

Published on: March 13, 2017

An Efficient Retrieval System Framework for Fabrics Based on Fine-Grained Similarity.

Jun Xiang¹, Ruru Pan¹, Weidong Gao¹

¹School of Textile Science & Engineering, Jiangnan University, No. 1800, Lihu Avenue, Wuxi 214122, China.

Entropy (Basel, Switzerland)

|September 23, 2022

Summary

This study introduces a novel deep learning method for fabric image retrieval, enhancing textile production efficiency. The DVSH model improves similarity embedding, addressing challenges in high-accuracy fabric matching for energy reduction goals.

Keywords:

deep hashing fabric retrieval fine-grained similarity similarity embedding variational network

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Last Updated: Aug 28, 2025

A Simple and Scalable Fabrication Method for Organic Electronic Devices on Textiles

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Published on: March 13, 2017

Fabricating Cotton Analytical Devices

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Published on: August 30, 2016

Deposition of Porous Sorbents on Fabric Supports

Deposition of Porous Sorbents on Fabric Supports

Published on: June 12, 2018

Area of Science:

Textile Engineering
Computer Vision
Artificial Intelligence

Background:

The textile industry faces significant energy consumption and emission reduction challenges within the "double carbon" context.
Improving production efficiency is crucial for the textile sector's sustainability.
Traditional image retrieval methods struggle with the high accuracy demands of fabric retrieval.

Purpose of the Study:

To propose a novel content-based image retrieval method for the textile industry.
To shorten the fabric production cycle by enhancing image retrieval accuracy.
To develop a robust hashing model for fine-grained fabric similarity measurement.

Main Methods:

Defined a fine-grained similarity metric for fabric images.
Designed a compact convolutional neural network with cross-domain connections.
Introduced a variational network and structural module into a hashing model, termed DVSH.
Employed list-wise learning for similarity embedding.

Main Results:

The proposed DVSH model demonstrates superior performance in fabric image retrieval.
The method effectively narrows the gap between fabric images and their similarities.
Experimental results validate the efficiency and superiority of the DVSH hashing model.

Conclusions:

The DVSH model offers an effective solution for accurate fabric image retrieval.
This technology can significantly improve production efficiency in the textile industry.
The approach contributes to the textile industry's energy-saving and emission-reduction efforts.