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Related Experiment Video

Updated: Jan 8, 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

Introducing an efficient method for feature extraction in image retrieval systems.

Xiaojun Zhao¹, Feng He^2,3, Kun Li¹

¹Special Equipment Inspection and Research Institute of Yunnan Province, Kunming, 650228, China.

Scientific Reports

|December 12, 2025

Summary

This summary is machine-generated.

This study enhances image retrieval systems by incorporating diverse features like texture and color, optimized using Black Hole Optimization (BHO) and Fuzzy C-Means (FCM) for faster, more accurate image searches.

Keywords:

Black hole optimization Feature extraction Fuzzy C-Means Gray level co-occurrence matrix Image retrieval

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Area of Science:

Computer Science
Information Retrieval
Artificial Intelligence

Background:

Image retrieval systems are crucial for managing large image databases and search engines.
Current systems often lack comprehensive features for accurate image description and retrieval.
Efficient image search enhances user access to information and overall search process efficiency.

Purpose of the Study:

To improve the performance of image retrieval systems by utilizing a wider set of descriptive features.
To enhance retrieval accuracy and processing speed through optimized feature selection.

Main Methods:

Image features were described using four categories: texture, color, and related patterns.
Black Hole Optimization (BHO) algorithm was employed for optimal feature subset selection.
Fuzzy C-Means (FCM) clustering was used for image retrieval based on the selected features.

Main Results:

The proposed method demonstrated significant retrieval precisions across multiple datasets.
Achieved precisions of 0.97 in Corel1000, 0.95 in Brodatz, and 0.94 in ALOI databases.
The integration of BHO and FCM improved both accuracy and processing speed.

Conclusions:

The developed approach effectively addresses limitations in current image retrieval systems.
Combining comprehensive feature description with optimized selection significantly boosts retrieval performance.
This method offers a robust solution for efficient and accurate content-based image retrieval.