Content-based image retrieval: A review of recent trends

• Published in: Cogent engineering Vol. 8; no. 1
• Main Authors: Hameed, Ibtihaal M., Abdulhussain, Sadiq H., Mahmmod, Basheera M.
• Format: Journal Article
• Language: English
• Published: Abingdon Cogent 01.01.2021; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Algorithms; content-based image retrieval; Digital imaging; Feature extraction; Image databases; image feature; Image management; Image retrieval; Machine learning; Performance evaluation; similarity measure; User requirements
• ISSN: 2331-1916; 2331-1916
• DOI: 10.1080/23311916.2021.1927469

With the availability of internet technology and the low-cost of digital image sensor, enormous amount of image databases have been created in different kind of applications. These image databases increase the demand to develop efficient image retrieval search methods that meet user requirements. Great attention and efforts have been devoted to improve content-based image retrieval method with a particular focus on reducing the semantic gap between low-level features and human visual perceptions. Due to the increasing research in this field, this paper surveys, analyses and compares the current state-of-the-art methodologies over the last six years in the CBIR field. This paper also provides an overview of CBIR framework, recent low-level feature extraction methods, machine learning algorithms, similarity measures, and a performance evaluation to inspire further research efforts.