An Overview of Content-Based Image Retrieval Methods and Techniques
With the development of Internet technology and the popularity of digital devices, Content-Based Image Retrieval (CBIR) has been quickly developed and applied in various fields related to computer vision and artificial intelligence. Currently, it is possible to retrieve related images effectively a...
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| Format: | Article |
| Language: | English |
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College of Education, Al-Iraqia University
2023-07-01
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| Series: | Iraqi Journal for Computer Science and Mathematics |
| Subjects: | |
| Online Access: | https://journal.esj.edu.iq/index.php/IJCM/article/view/579 |
| _version_ | 1797647575049830400 |
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| author | M.H.Hadid Qasim Mohammed Hussein Z.T.Al-Qaysi M.A.Ahmed Mahmood M. Salih |
| author_facet | M.H.Hadid Qasim Mohammed Hussein Z.T.Al-Qaysi M.A.Ahmed Mahmood M. Salih |
| author_sort | M.H.Hadid |
| collection | DOAJ |
| description |
With the development of Internet technology and the popularity of digital devices, Content-Based Image Retrieval (CBIR) has been quickly developed and applied in various fields related to computer vision and artificial intelligence. Currently, it is possible to retrieve related images effectively and efficiently from a large-scale database with an input image. In the past ten years, great efforts have been made for new theories and models of CBIR, and many effective CBIR algorithms have been established. Content-based image retrieval helps to discover identical images in a big dataset that match a query image. The query image's representative feature similarities to the dataset images typically assist in ranking the images for retrieval. There are various past studies on different handicraft feature descriptors according to the visual features that describe the images: color, texture, and shape. However, deep learning has been the dominant alternative to manually planned feature engineering; it automatically takes the features from the data. The current work reviews recent advancements in content-based image retrieval. For a deeper understanding of the advancement, the explanation of current state-of-the-art approaches from various vantage points is also conducted. This review employs a taxonomy encompassing various retrieval networks, classification types, and descriptors and this study will help researchers make more progress in image retrieval
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| first_indexed | 2024-03-11T15:19:29Z |
| format | Article |
| id | doaj.art-c200d5300f9b4fc495b0b1278c990220 |
| institution | Directory Open Access Journal |
| issn | 2958-0544 2788-7421 |
| language | English |
| last_indexed | 2024-03-11T15:19:29Z |
| publishDate | 2023-07-01 |
| publisher | College of Education, Al-Iraqia University |
| record_format | Article |
| series | Iraqi Journal for Computer Science and Mathematics |
| spelling | doaj.art-c200d5300f9b4fc495b0b1278c9902202023-10-29T06:11:51ZengCollege of Education, Al-Iraqia UniversityIraqi Journal for Computer Science and Mathematics2958-05442788-74212023-07-014310.52866/ijcsm.2023.02.03.006An Overview of Content-Based Image Retrieval Methods and Techniques M.H.Hadid0Qasim Mohammed Hussein1Z.T.Al-Qaysi 2M.A.Ahmed 3Mahmood M. Salih 4Tikrit University, College of Computer Science and Mathematics, Computer Science Department, IraqTikrit University, College of Computer Science and Mathematics, Computer Science Department, IraqTikrit University, College of Computer Science and Mathematics, Computer Science Department, IraqTikrit University, College of Computer Science and Mathematics, Computer Science Department, IraqTikrit University, College of Computer Science and Mathematics, Computer Science Department, Iraq With the development of Internet technology and the popularity of digital devices, Content-Based Image Retrieval (CBIR) has been quickly developed and applied in various fields related to computer vision and artificial intelligence. Currently, it is possible to retrieve related images effectively and efficiently from a large-scale database with an input image. In the past ten years, great efforts have been made for new theories and models of CBIR, and many effective CBIR algorithms have been established. Content-based image retrieval helps to discover identical images in a big dataset that match a query image. The query image's representative feature similarities to the dataset images typically assist in ranking the images for retrieval. There are various past studies on different handicraft feature descriptors according to the visual features that describe the images: color, texture, and shape. However, deep learning has been the dominant alternative to manually planned feature engineering; it automatically takes the features from the data. The current work reviews recent advancements in content-based image retrieval. For a deeper understanding of the advancement, the explanation of current state-of-the-art approaches from various vantage points is also conducted. This review employs a taxonomy encompassing various retrieval networks, classification types, and descriptors and this study will help researchers make more progress in image retrieval https://journal.esj.edu.iq/index.php/IJCM/article/view/579Deep learningMachine LearningCBIR |
| spellingShingle | M.H.Hadid Qasim Mohammed Hussein Z.T.Al-Qaysi M.A.Ahmed Mahmood M. Salih An Overview of Content-Based Image Retrieval Methods and Techniques Iraqi Journal for Computer Science and Mathematics Deep learning Machine Learning CBIR |
| title | An Overview of Content-Based Image Retrieval Methods and Techniques |
| title_full | An Overview of Content-Based Image Retrieval Methods and Techniques |
| title_fullStr | An Overview of Content-Based Image Retrieval Methods and Techniques |
| title_full_unstemmed | An Overview of Content-Based Image Retrieval Methods and Techniques |
| title_short | An Overview of Content-Based Image Retrieval Methods and Techniques |
| title_sort | overview of content based image retrieval methods and techniques |
| topic | Deep learning Machine Learning CBIR |
| url | https://journal.esj.edu.iq/index.php/IJCM/article/view/579 |
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