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Yıl 2021, Cilt: 5 Sayı: 1, 123 - 141, 15.04.2021
https://doi.org/10.35860/iarej.811927

Öz

Kaynakça

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  • 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.
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  • 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., J.-C. Burie, and J.-M. Ogier, Structured representations in a content based image retrieval context. Journal of Visual Communication and Image Representation, 2013. 24(8): p. 1252-1268.
  • 17. Sokic, E. and S. Konjicija. Novel fourier descriptor based on complex coordinates shape signature. in 2014 12th International Workshop on Content-Based Multimedia Indexing (CBMI). 2014.
  • 18. Bai, C., et al., K-means based histogram using multiresolution feature vectors for color texture database retrieval. Multimedia Tools and Applications, 2015. 74(4): p. 1469-1488.
  • 19. Bala, A. and T. Kaur, Local texton XOR patterns: A new feature descriptor for content-based image retrieval. Engineering Science and Technology, an International Journal, 2016. 19(1): p. 101-112.
  • 20. Guo, J. and H. Prasetyo, Content-Based Image Retrieval Using Features Extracted From Halftoning-Based Block Truncation Coding. IEEE Transactions on Image Processing, 2015. 24(3): p. 1010-1024.
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  • 22. Bakar, S.A., M.S. Hitam, and W.N.J.H.W. Yussof. Content-Based Image Retrieval using SIFT for binary and greyscale images. in 2013 IEEE International Conference on Signal and Image Processing Applications. 2013.
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  • 24. Montazer, G.A. and D. Giveki, Content based image retrieval system using clustered scale invariant feature transforms.Optik, 2015. 126(18): p. 1695-1699.
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  • 28. Seetharaman, K. and M. Kamarasan, Statistical framework for image retrieval based on multiresolution features and similarity method. Multimedia Tools and Applications, 2014. 73(3): p. 1943-1962.
  • 29. Rahimi, M. and M. Ebrahimi Moghaddam, A content-based image retrieval system based on Color Ton Distribution descriptors. Signal, Image and Video Processing, 2015. 9(3): p. 691-704.
  • 30. Yue, J., et al., Content-based image retrieval using color and texture fused features. Mathematical and Computer Modelling, 2011. 54(3): p. 1121-1127.
  • 31. Singh, N., K. Singh, and A.K. Sinha, A Novel Approach for Content Based Image Retrieval. Procedia Technology, 2012. 4: p. 245-250.
  • 32. Alsmadi, M.K., An efficient similarity measure for content based image retrieval using memetic algorithm. Egyptian Journal of Basic and Applied Sciences, 2017. 4(2): p. 112-122.
  • 33. Ayoobkhan, M.U.A., C. Eswaran, and K. Ramakrishnan, CBIR system based on prediction errors. Journal of Information Science and Engineering, 2017. 33(2): p. 347-365.
  • 34. Saeed, M.G., F.L. Malallah, and Z.A. Aljawaryy, Content-based image retrieval by multi-features extraction and k-means clustering. International Journal of Electrical, Electronics and Computers, 2017. 2(3): p. 1-11.
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  • 45. Sasikala, S. and R.S. Gandhi, Efficient Content Based Image Retrieval System with Metadata Processing. International Journal for Innovative Research in Science and Technology, 2015. 1(10): p. 72-77.
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  • 51. Liu, Y., et al., A survey of content based image retrieval with high-level semantics. Pattern Recognition, 2007. 40(1): p. 262-282.
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  • 56. Barbu, T. Content based image retrieval using gabor filtering. in 20th International Workshop on Database and Expert Systems Application. 2009.
  • 57. Juneja, K., et al. A survey on recent image indexing and retrieval techniques for low-level feature extraction in cbir systems. in IEEE International Conference on Computational Intelligence & Communication Technology. 2015.
  • 58. Mukane, S.M., S.R. Gengaje, and D.S. Bormane, A novel scale and rotation invariant texture image retrieval method using fuzzy logic classifier. Computers & Electrical Engineering, 2014. 40(8): p. 154-162.
  • 59. Hole, A.W. and P.L. Ramteke, Content Based Image Retrieval using Dominant Color and Texture features. International Journal of Advanced Research in Computer and Communication Engineering, 2015. 4(10): p. 45-49.
  • 60. Sadek, S., et al. Cubic-splines neural network- based system for Image Retrieval. in 2009 16th IEEE International Conference on Image Processing (ICIP). 2009.
  • 61. Hörster, E., R. Lienhart, and M. Slaney, Image retrieval on large-scale image databases, in Proceedings of the 6th ACM international conference on Image and video retrieval. 2007: Association for Computing Machinery: Amsterdam, The Netherlands. p. 17–24.
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An analysis of content-based image retrieval

Yıl 2021, Cilt: 5 Sayı: 1, 123 - 141, 15.04.2021
https://doi.org/10.35860/iarej.811927

Öz

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.

Kaynakça

  • 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., J.-C. Burie, and J.-M. Ogier, Structured representations in a content based image retrieval context. Journal of Visual Communication and Image Representation, 2013. 24(8): p. 1252-1268.
  • 17. Sokic, E. and S. Konjicija. Novel fourier descriptor based on complex coordinates shape signature. in 2014 12th International Workshop on Content-Based Multimedia Indexing (CBMI). 2014.
  • 18. Bai, C., et al., K-means based histogram using multiresolution feature vectors for color texture database retrieval. Multimedia Tools and Applications, 2015. 74(4): p. 1469-1488.
  • 19. Bala, A. and T. Kaur, Local texton XOR patterns: A new feature descriptor for content-based image retrieval. Engineering Science and Technology, an International Journal, 2016. 19(1): p. 101-112.
  • 20. Guo, J. and H. Prasetyo, Content-Based Image Retrieval Using Features Extracted From Halftoning-Based Block Truncation Coding. IEEE Transactions on Image Processing, 2015. 24(3): p. 1010-1024.
  • 21. Huang, M., et al., Content-based image retrieval technology using multi-feature fusion.Optik - International Journal for Light and Electron Optics, 2015. 126(19): p. 2144-2148.
  • 22. Bakar, S.A., M.S. Hitam, and W.N.J.H.W. Yussof. Content-Based Image Retrieval using SIFT for binary and greyscale images. in 2013 IEEE International Conference on Signal and Image Processing Applications. 2013.
  • 23. Matsui, Y., K. Aizawa, and Y. Jing. Sketch2Manga: Sketch-based manga retrieval. in 2014 IEEE International Conference on Image Processing (ICIP). 2014.
  • 24. Montazer, G.A. and D. Giveki, Content based image retrieval system using clustered scale invariant feature transforms.Optik, 2015. 126(18): p. 1695-1699.
  • 25. Chahooki, M.A.Z. and N.M. CharkariShape retrieval based on manifold learning by fusion of dissimilarity measures. IET Image Processing, 2012. 6, 327-336.
  • 26. Khodaskar, A.A. and S.A. Ladhake. A novel approach for content based image retrieval in context of combination S C techniques. in 2015 International Conference on Computer Communication and Informatics (ICCCI). 2015.
  • 27. Shrivastava, N. and V. Tyagi, Content based image retrieval based on relative locations of multiple regions of interest using selective regions matching. Information Sciences, 2014. 259: p. 212-224.
  • 28. Seetharaman, K. and M. Kamarasan, Statistical framework for image retrieval based on multiresolution features and similarity method. Multimedia Tools and Applications, 2014. 73(3): p. 1943-1962.
  • 29. Rahimi, M. and M. Ebrahimi Moghaddam, A content-based image retrieval system based on Color Ton Distribution descriptors. Signal, Image and Video Processing, 2015. 9(3): p. 691-704.
  • 30. Yue, J., et al., Content-based image retrieval using color and texture fused features. Mathematical and Computer Modelling, 2011. 54(3): p. 1121-1127.
  • 31. Singh, N., K. Singh, and A.K. Sinha, A Novel Approach for Content Based Image Retrieval. Procedia Technology, 2012. 4: p. 245-250.
  • 32. Alsmadi, M.K., An efficient similarity measure for content based image retrieval using memetic algorithm. Egyptian Journal of Basic and Applied Sciences, 2017. 4(2): p. 112-122.
  • 33. Ayoobkhan, M.U.A., C. Eswaran, and K. Ramakrishnan, CBIR system based on prediction errors. Journal of Information Science and Engineering, 2017. 33(2): p. 347-365.
  • 34. Saeed, M.G., F.L. Malallah, and Z.A. Aljawaryy, Content-based image retrieval by multi-features extraction and k-means clustering. International Journal of Electrical, Electronics and Computers, 2017. 2(3): p. 1-11.
  • 35. Joshi, K.D., S.N. Bhavsar, and R.C. Sanghvi, Image retrieval system using intuitive descriptors. Procedia Technology, 2014. 14: p. 535-542.
  • 36. Sharma, M. and A. Batra, Analysis of distance measures in content based image retrieval. Global Journal of Computer Science and Technology, 2014. 14(2): p. 7.
  • 37. Mistry, Y., D.T. Ingole, and M.D. Ingole, Content based image retrieval using hybrid features and various distance metric. Journal of Electrical Systems and Information Technology, 2018. 5(3): p. 874-888.
  • 38. Prasad, D. and V. Mukherjee, A novel symbiotic organisms search algorithm for optimal power flow of power system with FACTS devices. Engineering Science and Technology, an International Journal, 2016. 19(1): p. 79-89.
  • 39. Pradeep, S. and L. Malliga. Content based image retrieval and segmentation of medical image database with fuzzy values. in International Conference on Information Communication and Embedded Systems (ICICES2014). 2014.
  • 40. Katira, C., et al., Advanced content based image retrieval using multiple feature extraction. International Journal of Innovative Research in Science, Engineering and Technology, 2015. 4(10): p. 9805-9812.
  • 41. Gupta, E. and D.R. Kushwah, Combination of Local, Global and K-Mean using Wavelet Transform for Content Base Image Retrieval. International Journal of Computer Applications, 2015. 116(14): p. 5-9.
  • 42. Bagri, N. and P.K. Johari, A Comparative study on feature extraction using texture and shape for content based image retrieval. International Journal of Advanced Science and Technology, 2015. 80: p. 41 - 52.
  • 43. Giveki, D., et al., A New Content Based Image Retrieval Model Based on Wavelet Transform. Journal of Computer and Communications, 2015. Vol.03No.03: p. 8.
  • 44. Selvi, P., et al., Novel image retrieval approach in similarity integrated network using combined ranking algorithm. World Engineering & Applied Sciences Journal 2015. 6(3): p. 152-156.
  • 45. Sasikala, S. and R.S. Gandhi, Efficient Content Based Image Retrieval System with Metadata Processing. International Journal for Innovative Research in Science and Technology, 2015. 1(10): p. 72-77.
  • 46. Singh, S. and E.R. Rajput, Content based image retrieval using SVM, NN and KNN classification. International Journal of Advanced Research in Computer and Communication Engineering, 2015. 4(5): p. 549-552.
  • 47. Dharani, T. and I.L. Aroquiaraj. A survey on content based image retrieval. in 2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering. 2013.
  • 48. Smeulders, A.W.M., et al., Content based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Mach. Intell., 2000. 22(12): p. 1349–1380.
  • 49. Li, Z., et al., Large-scale retrieval for medical image analytics: A comprehensive review. Medical Image Analysis, 2018. 43: p. 66-84.
  • 50. Tarulatha, B., N. Shroff, and M.B. Chaudhary. VIBGYOR indexing technique for image mining. in International Conference on Data Mining and Advanced Computing (SAPIENCE). 2016.
  • 51. Liu, Y., et al., A survey of content based image retrieval with high-level semantics. Pattern Recognition, 2007. 40(1): p. 262-282.
  • 52. Siradjuddin, I.A., W.A. Wardana, and M.K. Sophan. Feature extraction using self-supervised convolutional autoencoder for content based image retrieval. in 2019 3rd International Conference on Informatics and Computational Sciences (ICICoS). 2019.
  • 53. Fadaei, S., A. Rashno, and E. Rashno. Content based image retrieval speedup. in 2019 5th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS). 2019.
  • 54. Naoufal, M. and N. M’barek. content based image retrieval based on color string coding and genetic algorithm. in 2020 1st International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET). 2020.
  • 55. Rudrappa, G. and N. Vijapur. Cloud classification using K-means clustering and content based image retrieval technique. in International Conference on Communication and Signal Processing (ICCSP). 2020.
  • 56. Barbu, T. Content based image retrieval using gabor filtering. in 20th International Workshop on Database and Expert Systems Application. 2009.
  • 57. Juneja, K., et al. A survey on recent image indexing and retrieval techniques for low-level feature extraction in cbir systems. in IEEE International Conference on Computational Intelligence & Communication Technology. 2015.
  • 58. Mukane, S.M., S.R. Gengaje, and D.S. Bormane, A novel scale and rotation invariant texture image retrieval method using fuzzy logic classifier. Computers & Electrical Engineering, 2014. 40(8): p. 154-162.
  • 59. Hole, A.W. and P.L. Ramteke, Content Based Image Retrieval using Dominant Color and Texture features. International Journal of Advanced Research in Computer and Communication Engineering, 2015. 4(10): p. 45-49.
  • 60. Sadek, S., et al. Cubic-splines neural network- based system for Image Retrieval. in 2009 16th IEEE International Conference on Image Processing (ICIP). 2009.
  • 61. Hörster, E., R. Lienhart, and M. Slaney, Image retrieval on large-scale image databases, in Proceedings of the 6th ACM international conference on Image and video retrieval. 2007: Association for Computing Machinery: Amsterdam, The Netherlands. p. 17–24.
  • 62. Alghamdi, R.A., M. Taileb, and M. Ameen. A new multimodal fusion method based on association rules mining for image retrieval. in MELECON 17th IEEE Mediterranean Electrotechnical Conference. 2014.
  • 63. Wang, B., et al. Saliency distinguishing and applications to semantics extraction and retrieval of natural image. in 2010 International Conference on Machine Learning and Cybernetics. 2010.
  • 64. Zhang, D., M. Monirul Islam, and G. Lu, Structural image retrieval using automatic image annotation and region based inverted file. Journal of Visual Communication and Image Representation, 2013. 24(7): p. 1087-1098.
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  • 75. Chu, L., et al., Robust spatial consistency graph model for partial duplicate image retrieval. IEEE Transactions on Multimedia, 2013. 15(8): p. 1982-1996.
  • 76. Xu, B., et al., EMR: A scalable graph-based ranking model for content-based image retrieval. IEEE Transactions on Knowledge and Data Engineering, 2015. 27(1): p. 102-114.
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  • 78. Yu, J., et al., Learning to rank using user clicks and visual features for image retrieval. IEEE Transactions on Cybernetics, 2015. 45(4): p. 767-779.
  • 79. Hsiao, K., J. Calder, and A.O. Hero, Pareto-depth for multiple-query image retrieval. IEEE Transactions on Image Processing, 2015. 24(2): p. 583-594.
  • 80. Tiakas, E., et al., MSIDX: multi-sort indexing for efficient content-based image search and retrieval. IEEE Transactions on Multimedia, 2013. 15(6): p. 1415-1430.
  • 81. Xiao, Z. and X. Qi, Complementary relevance feedback-based content-based image retrieval. Multimedia Tools and Applications, 2014. 73(3): p. 2157-2177.
  • 82. Lakshmi, A., M. Nema, and S. Rakshit, Long term relevance feedback: a probabilistic axis re-weighting update scheme. IEEE Signal Processing Letters, 2015. 22(7): p. 852-856.
  • 83. Kundu, M.K., M. Chowdhury, and S. Rota Bulò, A graph-based relevance feedback mechanism in content-based image retrieval. Knowledge-Based Systems, 2015. 73: p. 254-264.
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  • 85. Liu, F., et al., Intelligent and secure content-based image retrieval for mobile users. IEEE Access, 2019. 7: p. 119209-119222.
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Toplam 86 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yazılım Mühendisliği (Diğer)
Bölüm Review Articles
Yazarlar

Hakan Koyuncu 0000-0002-8444-1094

Manish Dixit Bu kişi benim 0000-0003-2589-6010

Baki Koyuncu Bu kişi benim 0000-0002-0507-3431

Yayımlanma Tarihi 15 Nisan 2021
Gönderilme Tarihi 20 Ekim 2020
Kabul Tarihi 12 Şubat 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 5 Sayı: 1

Kaynak Göster

APA Koyuncu, H., Dixit, M., & Koyuncu, B. (2021). An analysis of content-based image retrieval. International Advanced Researches and Engineering Journal, 5(1), 123-141. https://doi.org/10.35860/iarej.811927
AMA Koyuncu H, Dixit M, Koyuncu B. An analysis of content-based image retrieval. Int. Adv. Res. Eng. J. Nisan 2021;5(1):123-141. doi:10.35860/iarej.811927
Chicago Koyuncu, Hakan, Manish Dixit, ve Baki Koyuncu. “An Analysis of Content-Based Image Retrieval”. International Advanced Researches and Engineering Journal 5, sy. 1 (Nisan 2021): 123-41. https://doi.org/10.35860/iarej.811927.
EndNote Koyuncu H, Dixit M, Koyuncu B (01 Nisan 2021) An analysis of content-based image retrieval. International Advanced Researches and Engineering Journal 5 1 123–141.
IEEE H. Koyuncu, M. Dixit, ve B. Koyuncu, “An analysis of content-based image retrieval”, Int. Adv. Res. Eng. J., c. 5, sy. 1, ss. 123–141, 2021, doi: 10.35860/iarej.811927.
ISNAD Koyuncu, Hakan vd. “An Analysis of Content-Based Image Retrieval”. International Advanced Researches and Engineering Journal 5/1 (Nisan 2021), 123-141. https://doi.org/10.35860/iarej.811927.
JAMA Koyuncu H, Dixit M, Koyuncu B. An analysis of content-based image retrieval. Int. Adv. Res. Eng. J. 2021;5:123–141.
MLA Koyuncu, Hakan vd. “An Analysis of Content-Based Image Retrieval”. International Advanced Researches and Engineering Journal, c. 5, sy. 1, 2021, ss. 123-41, doi:10.35860/iarej.811927.
Vancouver Koyuncu H, Dixit M, Koyuncu B. An analysis of content-based image retrieval. Int. Adv. Res. Eng. J. 2021;5(1):123-41.



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