Fusion of local and global feature representation via optimised transfer learning approach on enhanced content-based image retrieval systems

Abstract

Currently, images are commonly used, and the number of metaphors, photos, and graphics being created is accelerating. The large volume of data requires a data outsourcing service, such as cloud computing and storage. In addition, it needs a content-based retrieval and exploration solution. The data has been enlarged enormously; however, content-based image retrieval was quite a tiresome job. Image Retrieval is vital because images are used in diverse applications, such as historical research, biodiversity, fingerprint identification, crime prevention, information systems, and medicine. The content-based image retrieval (CBIR) model is usually employed in these cases. From a vast dataset, the CBIR gathers images similar to the query image and removes less beneficial features, then delivers the resulting image as the query image. Next, it compares and matches these features with dataset image features and examines them with parallel features. This paper proposes an Enhanced Content-Based Image Retrieval Using Fusion of Feature Representation with Optimised Image Similarity Measures (CBIRFR-OISM) approach. The main objective is to effectively enhance the system’s capability to retrieve visually and contextually similar images. Initially, the CBIRFR-OISM approach applies image pre-processing using the Gaussian filter (GF) to accentuate edges and structural details. Next, a dual-path deep feature extractor is executed to obtain both local and global representations. The ResNet-50 model captures intricate local patterns, the Vision Transformer (ViT) extracts global contextual representations, and a heuristic search algorithm is employed to select the finest feature maps. The optimal features are then combined through an attention-based fusion model to form a fused feature vector. For image similarity measurement and retrieval, the Manhattan Distance metric is employed to compare feature vectors in the multi-dimensional space. The empirical results of the CBIRFR-OISM method are examined on Corel images, natural images, and the Fruits-360 dataset. The experimental validation of the CBIRFR-OISM method achieved superior precision of 82.32%, 86.66%, and 93.21% compared with existing models. Not applicable.