K-Nearest Neighbors and Support Vector Machine for Optimal Content-Based Image Retrieval with Low-Level Feature Fusion

Using a technique called content-based image retrieval, images can be found and retrieved from image databases according to their visual content. The absence of thorough representation is a major problem because different features frequently only capture some facets of the visual information. The application of K-Nearest Neighbors and Support Vector Machines, two well-liked machine learning methods, for CBIR. To create a thorough feature vector for every image, the suggested method extracts low-level data like color histograms, texture descriptors, and form features. These feature vectors are used to independently train the KNN and SVM classifiers, creating strong models that can predict picture similarity. The goal is to obtain a more comprehensive representation of the image content by combining the strengths of individual low-level elements through the use of a fusion technique. The study assesses and contrasts the retrieval accuracy, computational efficiency, and scalability of KNN and SVM. In order to attain improved retrieval efficiency, our suggested system makes use of a unique framework that incorporates texture, color, and shape information. To enable precise retrieval of the needed photos, the system gathers a wealth of significant, reliable, and comprehensive information from the image database and saves them in the repository.