Delving into the Depths of Image Retrieval Systems in the Light of Deep Learning: A Review

Objective: The objective of this study is to conduct a comprehensive review existing research and literature in the field Content-Based Image Retrieval (CBIR). This highlights key challenges associated with extraction representation visual semantics images. paper discusses measure used computing similarity ranking retrieved images by CBIR system. limitation traditional approaches also current deep learning methods semantic feature representation, defining metrics indexing. scalability generalization implementing real environment. Methods: A thorough was conducted on wellestablished databases, including Scopus, Web Science, IEEE Xplore, ACM, Science Direct, employing appropriate keywords. Mention period coverage. Pertinent search terms encompassed local global low-level features, high level gap, image embeddings, handcrafted learning, descriptors, similarity, indexing, aim exploring content-based retrieval systems. Comparative analysis performed chosen articles, taking into account factors such as algorithms, methodologies, datasets, evaluation metrics. results discussed using comparative analysis, ensuring overview recent retrieval, offering valuable insights highlighting emerging trends field. Findings : uncovers novelty realm contentbased (CBIR) challenge high-level when comparing images, perceived humans. It emphasizes that choices significantly influence system performance, stressing importance selecting suitable features measures based dataset characteristics application requirements. underscores persistent obstacle gap between concepts, encouraging exploration diverse like relevance feedback, ontology-based bridge gap. Particularly, learn-ing techniques, notably Convolutional Neural Networks (CNNs), have shown promising automatically hierarchical representations capturing information. However, scaling limited accessibility precisely labelled which can hinder performance across datasets real-world scenarios. Deep models pose interpretability due their complex, opaque nature representations. Keywords: CBIR; ContentBased Retrieval; Learning; Network; Local Features; Global Similarity Metric; Semantic Gap