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Enhanced content-based image retrieval via hybrid color, texture, and deep learning features
Surbhi Tyagi1, Praveen Shukla2, Partap Singh1
1Quantum University, Roorkee, Uttarakhand, India.
Scientific Reports
|March 25, 2026
Summary
CTD-Net, a novel Content-Based Image Retrieval system, enhances image search by combining color, texture, and deep learning features. This hybrid approach significantly improves retrieval precision across multiple datasets.
Area of Science:
- Computer Science
- Artificial Intelligence
- Image Processing
Background:
- Traditional Content-Based Image Retrieval (CBIR) systems face limitations in capturing complex visual and semantic information.
- Existing methods often rely solely on either handcrafted or deep learning features, hindering comprehensive image analysis.
Purpose of the Study:
- To introduce CTD-Net (Color, Texture, and Deep Learning- Network), a novel CBIR system.
- To enhance image retrieval performance by integrating diverse feature types.
Main Methods:
- CTD-Net fuses handcrafted features (Color Histogram, Color Moments, Local Binary Patterns, Wavelet Transform) with deep features (EfficientNet-B7).
- This hybrid approach bridges the gap between low-level visual attributes and high-level semantic understanding.
Main Results:
- CTD-Net achieved high precision rates: 98.85% on Corel-1K, 92.40% on Corel-10K, and 88.94% on Caltech-101.
- The system significantly outperformed existing CBIR methods in experimental evaluations.
Conclusions:
- Hybrid feature fusion in CTD-Net is highly effective for CBIR.
- The proposed approach demonstrates significant potential for advancing the field of Content-Based Image Retrieval.
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