Semantic content-based image retrieval: A comprehensive study

Ahmad Alzu'bi, Abbes Amira, Naeem Ramzan

Research output: Contribution to journalArticlepeer-review

150 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)20-54
JournalJournal of Visual Communication and Image Representation
Publication statusPublished - Oct 2015


  • CBIR
  • Image features
  • Dimensionality reduction
  • Deep learning
  • Relevance feedback
  • Image annotation
  • Visualization
  • Semantic gap


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