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Fabric Retrieval Based on Multi-Task Learning.

Jun Xiang, Ning Zhang, Ruru Pan

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|December 29, 2020

Summary

This study introduces a new method for fabric image retrieval using multi-task learning and deep hashing. The approach enhances accuracy for complex fabric appearances, outperforming existing techniques.

Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Fabric image retrieval is crucial for e-commerce and inventory management.
Current methods face challenges due to fabric appearance complexity and accuracy demands.

Purpose of the Study:

To develop a novel approach for accurate fabric image retrieval.
To address the limitations of existing Content Based Image Retrieval (CBIR) methods for fabrics.

Main Methods:

Proposed a multi-task learning model with uncertainty loss for fabric image representation.
Utilized unsupervised deep learning for encoding features into 128-bit hashing codes.
Employed hashing codes as an index for efficient image retrieval.

Main Results:

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The proposed method effectively learns fabric image representations.
Deep hashing generated efficient 128-bit codes for indexing.
Experimental results on an upgraded dataset demonstrated superior performance over state-of-the-art methods.

Conclusions:

The novel multi-task learning and deep hashing approach significantly improves fabric image retrieval.
This method offers a robust solution for complex fabric appearance challenges.
The enhanced dataset and approach provide a strong baseline for future research.