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Updated: Mar 27, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

Object-guided multi-granularity unsupervised hashing for image retrieval.

Zifan Liu¹, Yuan Cao¹, Xue Xu¹

¹Ocean University of China, Qingdao, 266100, Shandong Province, China.

Neural Networks : the Official Journal of the International Neural Network Society

|March 24, 2026

Summary

This study introduces Object-guided Multi-granularity Unsupervised Hashing (OMUH) for efficient image retrieval. OMUH significantly improves search accuracy by leveraging object detection to refine hashing strategies.

Keywords:

Image retrieval Object-guided Unsupervised hashing

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Last Updated: Mar 27, 2026

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Published on: January 5, 2024

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Unsupervised hashing methods for large-scale image retrieval face challenges with semantic similarity measurement.
Existing methods often use cosine similarity, which is insufficient for capturing nuanced image relationships.
Contrastive learning approaches struggle with unbalanced or inaccurate positive and negative examples.

Purpose of the Study:

To propose a novel unsupervised hashing method, Object-guided Multi-granularity Unsupervised Hashing (OMUH).
To enhance semantic similarity representation in image retrieval using object detection.
To improve the performance of unsupervised hashing by refining example selection for contrastive learning.

Main Methods:

Utilizing a pre-trained object detection model to generate pseudo-labels for images.
Reconstructing the affinity matrix by emphasizing object roles based on pseudo-labels.
Enlarging positive and purifying negative examples for contrastive learning using pseudo-labels.
Designing a multi-granularity objective function to preserve intra-image, inter-image, and intra-class similarities.

Main Results:

The proposed OMUH method demonstrates superior performance compared to state-of-the-art unsupervised hashing techniques.
Experiments on three benchmark datasets show significant improvements, with mean Average Precision (mAP) increases up to 12%.
The object-guided approach effectively enhances the semantic understanding for more accurate hash code learning.

Conclusions:

OMUH offers a robust and effective solution for unsupervised hashing in large-scale image retrieval.
Leveraging object detection provides a powerful mechanism to overcome limitations of traditional similarity measures.
The method's ability to refine contrastive learning examples and preserve diverse similarities leads to substantial performance gains.