Semantic content-based image retrieval: A comprehensive study

• A comprehensive survey on content-based image retrieval (CBIR) is introduced.
• Important challenges of CBIR are discussed, e.g. semantic gap and curse of dimensionality.
• Recent achievements chiefly in the context of deep learning and automatic tagging are explained.
• New research trends and future insights into the CBIR domain are highlighted.

The complexity of multimedia contents is significantly increasing in the current digital world. This yields an exigent demand for developing highly effective retrieval systems to satisfy human needs. Recently, extensive research efforts have been presented and conducted in the field of content-based image retrieval (CBIR). The majority of these efforts have been concentrated on reducing the semantic gap that exists between low-level image features represented by digital machines and the profusion of high-level human perception used to perceive images. Based on the growing research in the recent years, this paper provides a comprehensive review on the state-of-the-art in the field of CBIR. Additionally, this study presents a detailed overview of the CBIR framework and improvements achieved; including image preprocessing, feature extraction and indexing, system learning, benchmarking datasets, similarity matching, relevance feedback, performance evaluation, and visualization. Finally, promising research trends, challenges, and our insights are provided to inspire further research efforts.