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Updated: Jul 24, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

An Efficient Supervised Deep Hashing Method for Image Retrieval.

Abid Hussain¹, Heng-Chao Li¹, Muqadar Ali¹

¹School of Computing and Artificial Intelligence, Southwest Jiao Tong University, Chengdu 611731, China.

Entropy (Basel, Switzerland)

|July 8, 2023

Summary

This summary is machine-generated.

Researchers developed a new deep hashing method using convolutional neural networks (CNNs) and multiple nonlinear projections for efficient image retrieval. This approach improves accuracy and flexibility over traditional single-projection hashing techniques.

Keywords:

convolutional neural network deep learning deep supervised hashing image retrieval

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Efficient image retrieval from large databases is a significant challenge.
Current hashing methods often use single linear projections, limiting flexibility and causing optimization issues.

Purpose of the Study:

To introduce a novel CNN-based hashing method utilizing multiple nonlinear projections for enhanced image retrieval.
To develop an end-to-end deep hashing system for improved performance.

Main Methods:

A convolutional neural network (CNN) architecture was employed for hashing.
Multiple nonlinear projections were used to generate short-bit binary codes.
A specialized loss function was designed to preserve image similarity and minimize quantization error.

Main Results:

The proposed method demonstrated superior performance compared to existing state-of-the-art deep hashing techniques.
Experiments on various datasets confirmed the effectiveness and significance of the approach.
The method successfully maintained image similarity while minimizing quantization errors.

Conclusions:

The developed CNN-based hashing method with multiple nonlinear projections offers a more flexible and effective solution for large-scale image retrieval.
The proposed system and loss function contribute to advancements in deep hashing research.