Semantic Based Image Indexing

Abstract

This paper introduces a semantic-based image indexing system utilizing a custom Convolutional Neural Network (CNN) for feature extraction and semantic embedding techniques for understanding image content. Traditional image indexing methods rely heavily on low-level visual features, often resulting in inaccurate or irrelevant results. By leveraging deep learning, our proposed system bridges this gap, allowing high-level semantic features to guide indexing and retrieval. Tested on the CIFAR-10 dataset, our approach demonstrates a significant improvement in precision, recall, and overall retrieval performance, showcasing its potential in real-world applications.

Introduction

The rapid increase in digital images demands efficient indexing and retrieval systems that overcome the "semantic gap"—the disconnect between low-level image features (color, texture) and high-level human understanding (e.g., "dog playing in the park"). This research develops a semantic-based image indexing system using deep learning, specifically a custom CNN on the CIFAR-10 dataset, to extract meaningful features and combine them with semantic embeddings for accurate, scalable image retrieval.

Key challenges addressed include representing complex semantic content, understanding context, handling large-scale datasets, automating annotation, and effectively interpreting user queries. The system is modular, consisting of:

• Image Preprocessing for cleaning and standardizing images.

• Feature Extraction using traditional and deep learning methods to capture visual patterns.

• Training and Callbacks to optimize model learning.

• Embedding and Similarity modules that transform features into compact vectors and measure semantic similarity.

• Main Execution and Front-End Modules that manage processing and provide an intuitive user interface supporting text, image, or hybrid queries.

• Image Retrieval that ranks images by semantic relevance.

• Model Evaluation and Fine-Tuning to measure accuracy, relevance, scalability, and responsiveness.

• Final Integration that unifies all components for real-world deployment, ensuring scalability, performance, and user feedback incorporation.

This approach aims to enhance search relevance across multimedia databases, e-commerce, and autonomous systems by bridging semantic gaps through advanced deep learning techniques.

Conclusion

This paper presents a robust semantic-based image indexing system designed to enhance retrieval accuracy. Using a custom CNN model, we successfully mapped images to high-level semantic representations, enabling precise and efficient retrieval. The system outperformed traditional methods in both quantitative and qualitative evaluations, demonstrating its applicability to real-world scenarios. Future work will focus on scaling the system to larger datasets, incorporating multi-modal data, and exploring real-time retrieval solutions.

References

[1] Zhu, L., & Zhang, L. (2020). \"Deep learning-based image retrieval: A survey.\" Journal of Visual Communication and Image Representation, 74, 102912. [2] Liu, X., & Xu, Z. (2021). \"Semantic Image Retrieval Using Deep Convolutional Features and Enhanced Tagging.\" IEEE Transactions on Image Processing, 30, 1248-1259. [3] Zhang, Z., & Liu, S. (2022). \"A semantic-driven deep learning approach for image indexing and retrieval.\" Neurocomputing, 476, 90-101. [4] Chen, Y., & Zhao, J. (2023). \"Efficient semantic-based image retrieval using multi-scale feature fusion.\" Pattern Recognition, 131, 107755

Copyright

Copyright © 2025 Sujal Fethfulwar, Drushya Bhaisare, Yash Nakhale, Ridam Lokhande, Aniket Gongale, Dr. Dhananjay Dumbere. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.