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Distributed search and fusion for wine label image retrieval.

Xiaoqing Li^1,2, Jinwen Ma²

¹School of Statistics, Capital University of Economics and Business, Beijing, China.

Peerj. Computer Science

|October 20, 2022

Summary

This study introduces two distributed retrieval frameworks to improve wine label image retrieval. These methods effectively address data imbalance challenges, outperforming existing models on large datasets.

Keywords:

Distributed strategy Inter-class imbalance Wine label image retrieval

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Wine culture is growing, increasing the need for efficient wine label image retrieval.
Deep learning models for retrieval struggle with the large number of wine brands and imbalanced sample data.

Purpose of the Study:

To propose effective distributed retrieval frameworks for wine label image retrieval.
To address the challenge of imbalanced sample data in wine brand identification.

Main Methods:

Developed two novel distributed retrieval frameworks.
Utilized a distributed strategy to handle large-scale datasets and data imbalance.
Evaluated performance on a large-scale wine label dataset and the Oxford flowers dataset.

Main Results:

Both proposed distributed retrieval frameworks demonstrated significant effectiveness.
The frameworks outperformed previous state-of-the-art retrieval models.
Successful retrieval of wine information, including brand and sub-brand, was achieved.

Conclusions:

The proposed distributed retrieval frameworks are effective solutions for wine label image retrieval.
These methods successfully overcome the limitations of imbalanced data in deep learning retrieval systems.
The approach shows strong potential for real-world applications in wine information systems.