CONTENT BASED IMAGE RETRIEVAL FOR SUPER RESOLTUON IMAGES USING FEATURE FUSION: DEEP LEARNING AND HAND CRAFTED

Abstract

Content-Based Image Retrieval (CBIR) has become an essential technique for efficiently searching and retrieving images from largescale databases based on visual content rather than textual annotations. With the increasing demand for high-quality images, super-resolution techniques have gained attention for enhancing image details and clarity. However, retrieving relevant highresolution images remains a challenging task due to variations in texture, color, and structural details. This project proposes a hybrid approach for CBIR in superresolution images by combining deep learning features with handcrafted features through feature fusion. The proposed system utilizes Convolutional Neural Networks (CNNs) to extract deep features that capture high-level semantic information from images. Simultaneously, handcrafted features such as color histograms, texture descriptors (e.g., Local Binary Patterns), and edge features are extracted to preserve lowlevel visual details. These complementary features are fused to create a robust feature representation, improving retrieval accuracy. The system also incorporates superresolution techniques to enhance image quality before feature extraction, ensuring better feature representation and matching. The methodology involves preprocessing image datasets, applying super-resolution models, extracting deep and handcrafted features, and performing similarity matching using distance metrics such as Euclidean distance or cosine similarity. Experimental results demonstrate that the feature fusion approach significantly improves retrieval performance compared to using either deep learning or handcrafted features alone. The system achieves higher precision and recall rates, making it suitable for applications such as medical imaging, surveillance, and multimedia search systems.