Journal
JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
Volume 32, Issue -, Pages 20-54Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jvcir.2015.07.012
Keywords
CBIR; Image features; Dimensionality reduction; Deep learning; Relevance feedback; Image annotation; Visualization; Semantic gap
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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. (C) 2015 Elsevier Inc. All rights reserved.
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