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Dergiler / International Advanced Researches and Engineering Journal / 2021 / Cilt: 5 - Sayı: 1

An analysis of content-based image retrieval

Hakan KOYUNCU
Manish DİXİT
Baki KOYUNCU
Sayfa
123–141
Yayın Tarihi
2022-10-19
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

A total of 38 CBIR articles were comparatively analyzed.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Nowadays, working on digital images is gaining much popularity in multimedia systems, due to the rapid increase in the utilization of large image databases.
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Temel Bulgular & Veri:Thus, the Content-Based Image Retrieval (CBIR) method has become the most valuable method for these databases.
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Sonuç & Katkı:A total of 38 CBIR articles were comparatively analyzed.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Content-Based Image Retrieval (CBIR) · Feature Extraction · Performance measure

Abstract

Nowadays, working on digital images is gaining much popularity in multimedia systems, due to the rapid increase in the utilization of large image databases. Thus, the Content-Based Image Retrieval (CBIR) method has become the most valuable method for these databases. This study mainly focuses on content-based image retrieval; which uses image features like color, shape, texture, etc. by searching the user query image from a large image database based on user request. CBIR is the most widely used technique as its searching capability is faster than the other traditional methods, and it works well in retrieving images automatically. It is also a big alternative approach to traditional methods. The CBIR techniques are used in many applications like surveillance detection, crime avoidance, fingerprint identification, E-library, medical, historical monument and biodiversity information systems, and many more. A total of 38 CBIR articles were comparatively analyzed.

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Bu Makaleye Atıf Yap

KOYUNCU, H., DİXİT, M., KOYUNCU, B. (2022). An analysis of content-based image retrieval. International Advanced Researches and Engineering Journal, Cilt: 5 - Sayı: 1, 123–141. https://ajindex.com/articles/539318

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Kaynakça (16)

  1. Alzu’bi, A., A. Amira, and N. Ramzan, Semantic content-based image retrieval: A comprehensive study. Journal of Visual Communication and Image Representation, 2015. 32: p. 20-54
  2. Li, X., et al., Socializing the Semantic Gap. ACM Computing Surveys, 2016. 49(1): p. 1-39
  3. Lin, Z., et al. Semantics-preserving hashing for cross-view retrieval. in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2015. IEEE
  4. Lowe, D.G., Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision, 2004. 60(2): p. 91-110
  5. Feng, J., Y. Liu, and L. Wu, Bag of Visual Words Model with Deep Spatial Features for Geographical Scene Classification. Computational Intelligence and Neuroscience, 2017. 2017: p. 5169675
  6. An, J., S.H. Lee, and N.I. Cho. Content-based image retrieval using color features of salient regions. in 2014 IEEE International Conference on Image Processing (ICIP). 2014
  7. Philbin, J., et al. Object retrieval with large vocabularies and fast spatial matching. in 2007 IEEE Conference on Computer Vision and Pattern Recognition. 2007
  8. Hamouchene, I. and S. Aouat, A new approach for texture segmentation based on NBP method. Multimedia Tools and Applications, 2017. 76(2): p. 1921-1940
  9. Sun, S., et al., Scalable Object Retrieval with Compact Image Representation from Generic Object Regions. ACM Trans. Multimedia Comput. Commun. Appl., 2015. 12(2): p. Article 29
  10. Jenni, K., S. Mandala, and M.S. Sunar, Content Based Image Retrieval Using Colour Strings Comparison. Procedia Computer Science, 2015. 50: p. 374-379
  11. Liu, M., L. Yang, and Y. Liang, A chroma texture-based method in color image retrieval.Optik, 2015. 126(20): p. 2629-2633
  12. Batko, M., et al., Content-based annotation and classification framework: a general multi-purpose approach, in Proceedings of the 17th International Database Engineering & Applications Symposium. 2013, Association for Computing Machinery: Barcelona, Spain. p. 58–67
  13. Cheng, Z., J. Shen, and H. Miao, The effects of multiple query evidences on social image retrieval. Multimedia Systems, 2016. 22(4): p. 509-523
  14. Zhang, H., et al., Attribute-Augmented Semantic Hierarchy: Towards a Unified Framework for Content-Based Image Retrieval. ACM Trans. Multimedia Comput. Commun. Appl., 2014. 11(1s): p. Article 21
  15. Papushoy, A. and A.G. Bors, Image retrieval based on query by saliency content. Digital Signal Processing, 2015. 36: p. 156-173
  16. Raveaux, R.