Abstract :

The Rapid Growth Of Digital Image Repositories Has Created A Critical Need For Efficient And Intelligent Image Retrieval Systems. Traditional Keyword-based Approaches Suffer From Limitations Such As Dependency On Manual Annotations, Subjectivity, And Lack Of Semantic Understanding. To Address These Challenges, This Work Presents A Robust Content-Based Image Retrieval (CBIR) System Powered By Deep Learning Techniques. The Proposed System Leverages Pre-trained Convolutional Neural Networks (CNNs) To Automatically Extract Highlevel Feature Representations From Images, Capturing Semantic Information Such As Objects, Textures, And Spatial Patterns. These Deep Features Are Transformed Into Compact Feature Vectors And Stored In A Database For Efficient Indexing. Upon Receiving A Query Image, The System Computes Similarity Using Advanced Distance Metrics Such As Cosine Similarity Or Euclidean Distance To Retrieve The Most Relevant Images. Compared To Conventional Methods Relying On Low-level Features, The Proposed Approach Significantly Enhances Retrieval Accuracy, Relevance, And Scalability. The System Also Ensures Fast Retrieval Performance And Provides An Intuitive User Experience By Eliminating The Need For Textual Input. This Research Demonstrates The Effectiveness Of Deep Learning In Bridging The Semantic Gap In Image Retrieval And Highlights Its Potential Applications In Domains Such As Medical Imaging, Digital Libraries, E-commerce, And Multimedia Systems.

Published:

17-4-1-2026

Issue:

Vol. 26 No. 4-1 (2026)

Page Nos:

602-607

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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