An improved similarity matching model for the content-based image retrieval model

Content-based retrieval (CBR) is an essential process to retrieve images from databases based on metadata from the image. Metadata in an image refers to image colors, textures, and shapes, or any other important information that can be derived from the image itself. The goal of CBR is to search for the relevant image and retrieve it from databases. Many CBR models achieved reliable results in analyzing the image. However, the computation cost of image retrieval is a challenging task due to the growth of image traits. This paper presents an Optimized Hybrid Ensemble Model (OHEM) R-2,C-1. The proposed model aims to improve the process of similarity matching for the efficient analysis of the query image while minimizing computation time. The purpose of OHEM is twofold. First, OHEM analyzes the features within the query image and performs similarity matching within the database, achieving this with reduced computational complexity. Subsequently, in accordance with the established objective function, it identifies and evaluates the pertinent features. Two distinct datasets, ROxford and RParis, are utilized to assess the model's performance. Several assessment criteria, including F1-score, recall, precision, and computation time, have been used to assess the model. The computation and evaluated outcomes are compared to six distinct algorithms, such as CSM, equilibrium propagation (EP), DCM, GA-based IR, and IRT. The comparison findings suggested that the proposed method performs better than the other models. R-2,C-1.