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Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.

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Related Experiment Video

Updated: Jan 10, 2026

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

AI powered multi feature fusion framework for retrieving images using color, texture and shape descriptors.

Kommu Naveen¹, R M S Parvathi²

¹Department of Electronics & Communication Engineering, MAM School of Engineering, Trichy, Chennai Trunk Road, Siruganur, Tiruchirappalli, Tamil Nadu-621 105, India.

Scientific Reports

|November 29, 2025

Summary

This study introduces an AI framework for advanced Content-Based Image Retrieval (CBIR), integrating diverse visual features to enhance accuracy and semantic matching for digital image management.

Keywords:

Analysis CNN (Convolutional neural network)Characteristics of shape SVM stands for support vector machine Textures

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Last Updated: Jan 10, 2026

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Area of Science:

Computer Science
Artificial Intelligence
Information Retrieval

Background:

Digital visual content is rapidly expanding, necessitating advanced retrieval systems.
Existing Content-Based Image Retrieval (CBIR) methods suffer from semantic mismatches and low accuracy due to isolated feature sets.
A novel AI-driven framework is proposed to address these limitations.

Purpose of the Study:

To develop an improved CBIR framework by integrating form, texture, and color features.
To enhance semantic matching and retrieval accuracy in large image datasets.
To create a more scalable and efficient image retrieval solution.

Main Methods:

Deep feature fusion combining color moments, Gray-Level Co-occurrence Matrix (GLCM) for texture, and Fourier/Hu moments for form.
Support Vector Machine (SVM) classifiers and Convolutional Neural Networks (CNNs) for feature integration into a learned similarity space.
Attention-guided weighting to dynamically adjust feature importance based on query context.

Main Results:

The proposed hybrid AI-enhanced CBIR model achieved a maximum accuracy of 92.3% on the Corel-1K dataset.
Demonstrated superior performance over standard CBIR models in accuracy, recall, and mean Average Precision (mAP).
Consistent performance across various image categories and noise levels was observed.

Conclusions:

The integrated AI approach significantly improves Content-Based Image Retrieval accuracy and semantic relevance.
This framework offers a scalable solution for digital asset management, medical imaging, and multimedia search.
Combining deep learning with low-level visual descriptors is key for next-generation image retrieval systems.