Content-based Image Retrieval - Techniques and Applications: Exploring content-based image retrieval techniques for searching and retrieving images from large databases based on visual similarity

Authors

  • Dr. Akiko Yoshikawa Associate Professor of Mechanical Engineering, Tokyo Institute of Technology, Japan Author

Keywords:

Content-based image retrieval

Abstract

Content-based image retrieval (CBIR) has emerged as a vital area of research due to the exponential growth of digital image collections. This paper provides a comprehensive review of CBIR techniques and their applications. We first introduce the concept of CBIR and discuss its importance in various domains. Next, we delve into the key components of CBIR systems, including feature extraction, image representation, similarity measurement, and indexing strategies. We then review the state-of-the-art CBIR techniques, such as deep learning-based approaches, and discuss their advantages and limitations. Finally, we present some applications of CBIR in real-world scenarios, including medical image analysis, surveillance, and multimedia content management. This paper aims to provide researchers and practitioners with a thorough understanding of CBIR techniques and their potential applications.

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References

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Published

04-04-2022

How to Cite

[1]
Dr. Akiko Yoshikawa, “Content-based Image Retrieval - Techniques and Applications: Exploring content-based image retrieval techniques for searching and retrieving images from large databases based on visual similarity”, J. of Artificial Int. Research and App., vol. 2, no. 1, pp. 132–141, Apr. 2022, Accessed: Nov. 24, 2024. [Online]. Available: https://aimlstudies.co.uk/index.php/jaira/article/view/164

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