Content-Based Image Retrieval and Feature Extraction: A Comprehensive Review
Joint Authors
Ali, Nouman
Ratyal, Naeem
Zafar, Bushra
Latif, Afshan
Rasheed, Aqsa
Sajid, Umer
Ahmed, Jameel
Dar, Saadat Hanif
Sajid, Muhammad
Khalil, Tehmina
Source
Mathematical Problems in Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-21, 21 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-08-26
Country of Publication
Egypt
No. of Pages
21
Main Subjects
Abstract EN
Multimedia content analysis is applied in different real-world computer vision applications, and digital images constitute a major part of multimedia data.
In last few years, the complexity of multimedia contents, especially the images, has grown exponentially, and on daily basis, more than millions of images are uploaded at different archives such as Twitter, Facebook, and Instagram.
To search for a relevant image from an archive is a challenging research problem for computer vision research community.
Most of the search engines retrieve images on the basis of traditional text-based approaches that rely on captions and metadata.
In the last two decades, extensive research is reported for content-based image retrieval (CBIR), image classification, and analysis.
In CBIR and image classification-based models, high-level image visuals are represented in the form of feature vectors that consists of numerical values.
The research shows that there is a significant gap between image feature representation and human visual understanding.
Due to this reason, the research presented in this area is focused to reduce the semantic gap between the image feature representation and human visual understanding.
In this paper, we aim to present a comprehensive review of the recent development in the area of CBIR and image representation.
We analyzed the main aspects of various image retrieval and image representation models from low-level feature extraction to recent semantic deep-learning approaches.
The important concepts and major research studies based on CBIR and image representation are discussed in detail, and future research directions are concluded to inspire further research in this area.
American Psychological Association (APA)
Latif, Afshan& Rasheed, Aqsa& Sajid, Umer& Ahmed, Jameel& Ali, Nouman& Ratyal, Naeem…[et al.]. 2019. Content-Based Image Retrieval and Feature Extraction: A Comprehensive Review. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-21.
https://search.emarefa.net/detail/BIM-1200780
Modern Language Association (MLA)
Latif, Afshan…[et al.]. Content-Based Image Retrieval and Feature Extraction: A Comprehensive Review. Mathematical Problems in Engineering No. 2019 (2019), pp.1-21.
https://search.emarefa.net/detail/BIM-1200780
American Medical Association (AMA)
Latif, Afshan& Rasheed, Aqsa& Sajid, Umer& Ahmed, Jameel& Ali, Nouman& Ratyal, Naeem…[et al.]. Content-Based Image Retrieval and Feature Extraction: A Comprehensive Review. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-21.
https://search.emarefa.net/detail/BIM-1200780
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1200780