CONTENT BASED IMAGE RETRIEVAL SYSTEM USING DEEP LEARNING CNN WITH RESNET50 BACKBONE

Introduction: Content-Based Image Retrieval (CBIR) systems aim to retrieve relevant images from large databases based on visual content rather than metadata. Traditional CBIR techniques often struggle with accuracy due to limited feature representation. Recent advances in deep learning offer new opportunities for extracting high-level features. This work explores the integration of deep neural networks into CBIR. Specifically, it utilizes the ResNet50 architecture for enhanced image understanding and retrieval. Objectives: The primary objective is to develop an efficient and accurate CBIR system using deep learning. It aims to leverage the ResNet50 model to extract meaningful image features. The system is designed to improve retrieval accuracy over traditional methods. It seeks to handle diverse image content with flexibility. The goal is to display the top 10 visually similar images for any given query image. Methods: A pre-trained ResNet50 model is used to extract high-level features from a curated dataset of 1,000 images. These features are stored for comparison during retrieval. When a user submits a query image, features are extracted and compared using cosine similarity. This approach combines deep feature extraction with similarity-based ranking for effective image retrieval. Results: The proposed system integrates Deep Learning using CNN and ResNet50 with cosine similarity to perform efficient image retrieval. The model accurately extracts and compares features, ensuring meaningful retrieval results. Figures 3–6 demonstrate sample trials, showcasing the system's ability to retrieve and rank images based on cosine similarity values. Conclusions: This research presents a CBIR system enhanced with deep learning using the ResNet50 model for accurate image retrieval. When a user submits a query image, the system retrieves and ranks the top 10 most similar images using cosine similarity. By leveraging deep learning, the system captures complex visual features, offering improved accuracy over traditional CBIR methods.