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A comprehensive assessment of content-based image retrieval using selected full reference image quality assessment algorithms

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Abstract

In the area of full-reference (FR) objective ‘image quality assessment’ (IQA), a huge amount of improvement has been done in the last few years that can calculate image quality, consistently. Contrarily, query-based image/video databases and search engines retrieve related data using ‘ranking and indexing’ depending on stored content. It is also to be noted that both techniques use feature extraction to achieve their goal. The efficiency of seven selected FR-IQA schemes is described in this article for the retrieval of an image signal. Extensive tests are done on two freely accessible databases. The comparison results express that mean-structural-similarity-index-measure (MSSIM), and feature-similarity-index-measure (FSIM) offer superior outcome than other IQA models. Our assessment outcome and the related thought will be very much useful for the researchers to understand the latest application areas of the IQA model in the area of image searching and retrieval.

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References

  1. Al-Mohamade A, Bchir O, Ismail MMB (2020) Multiple query content-based image retrieval using relevance feature weight learning. J Imaging 6(1):1–13

    Article  Google Scholar 

  2. Bekhet S, Ahmed A (2020) Evaluation of similarity measures for video retrieval. Multimed Tools Appl 79:6265–6278

    Article  Google Scholar 

  3. Chu K, Liu GH, (2020) Image retrieval based on a multi-integration features model. Mathematical Problems in Engineering, https://doi.org/10.1155/2020/1461459.

  4. Chua T, Tang J, Hong R, Li H, Luo Z, Zheng Y (2009) NUSWIDE: a real-world web image database from National University of Singapore. In: ACM International Conference on Image and Video Retrieval, Greece, pp. 1–9. http://dl.nextcenter.org/public/nuswide/Flickr.tar.gz

  5. Duan G, Yang J, Yang Y (2011) Content-based image retrieval research. In: Proc. of the International Conference on Physics Science and Technology, Dubai, pp. 471–477.

  6. Eom M, Choe Y (2007) Fast extraction of edge histogram in DCT domain based on MPEG-7. Int J Electr Comput Electron Commun Eng 1(9):1385–1388

    Google Scholar 

  7. Hiremath PS, Pujari J (2007) Content based image retrieval using color, texture and shape features. In: Proc. of the 15th international conference on advanced computing and communications, United States, pp. 780–784.

  8. Jiang J, Armstrong A, Feng GC (2002) Direct content access and extraction from JPEG compressed images. Pattern Recogn 35:2511–2519

    Article  Google Scholar 

  9. Jose S, Joseph P (2014) Content based image retrieval system with watermarks and relevance feedback. Int J Comput Appl 99(11):1–6

    Google Scholar 

  10. Kumar V, Tripathi V, Pant B(2020)Content based fine-grained image retrieval using convolutional neural network. In. Proc. 7th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India, pp. 1120-1125.

  11. Latif A, Rasheed A, Sajid U, Ahmed J, Ali N, Ratyal NI, Zafar B, Dar SH, Sajid M, Khalil T (2019) Content-based image retrieval and feature extraction: a comprehensive review. Mathematical problems in engineering. https://doi.org/10.1155/2019/9658350.

  12. Li Z, Tang J (2015) Weakly supervised deep metric learning for community-contributed image retrieval. IEEE Trans Multimed 17(11):1989–1999

    Article  Google Scholar 

  13. Li Z, Liu J, Tang J, Lu H (2015) Robust structured subspace learning for data representation. IEEE Trans Pattern Anal Mach Intell 37(10):2085–2098

    Article  Google Scholar 

  14. Liu X, Zhang X (2019) NOMA-based resource allocation for cluster-based cognitive industrial internet of things. IEEE Trans Ind Inf 16(8):5379–5388

    Article  Google Scholar 

  15. Liu Y, Zhang D, Lu G, Ma W (2007) A survey of content-based image retrieval with high level semantics. Pattern Recogn 40(1):262–282

    Article  Google Scholar 

  16. Liu X, Jia M, Zhang X, Lu W (2019) A novel multichannel internet of things based on dynamic spectrum sharing in 5g communication. IEEE Internet Things J 6(4):5962–5970

    Article  Google Scholar 

  17. Lu ZM, Skibbe H, Burkhardt H (2005) Image retrieval based on a multipurpose watermarking scheme. In: Khosla R., Howlett R.J., Jain L.C. (eds) knowledge-based intelligent information and engineering systems. Lect Notes Comput Sci 3682:573–579

    Article  Google Scholar 

  18. Malik F, Baharudin B (2013) Analysis of distance metrics in content-based image retrieval using statistical quantized histogram texture features in the DCT domain. J King Saud Univ Comput Inf Sci 25:207–218

    Google Scholar 

  19. Malik F, Baharudin B (2013) The statistical quantized histogram texture features analysis for image retrieval based on median and Laplacian filters in the DCT domain. Int Arab J Inf Technol 10(6):616–624

    Google Scholar 

  20. Manjunath BS, Ohm JR, Vasudevan VV, Yamada A (2001) Color and texture descriptors. IEEE Trans Circuits Syst Vid Technol 11(6):703–715

    Article  Google Scholar 

  21. Mansoor Hussain D, Surendran D (2020) Content based image retrieval using bees algorithm and simulated annealing approach in medical big data applications. Multimed Tools Appl 79:3683–3698

    Article  Google Scholar 

  22. Mohamed A, Khellfi F, Weng Y, Jiang J, Ipson S (2009) An efficient image retrieval through DCT histogram quantization. In: Proceedings of the International Conference on Cyber Worlds, United Kingdom, pp. 237–240

  23. Mokhtar F, Ngadiran R, Basheer T, Abdul Rahim AN (2019) Analysis of wavelet-based full reference image quality assessment algorithm. Bull Electric Eng Inf 8(2):527–532

    Google Scholar 

  24. Nezamabadi-pour H, Saryazdi S (2007) Object-based image indexing and retrieval in DCT domain using clustering techniques. Int J Comput Control Quantum Inf Eng 1(3):759–762

    Google Scholar 

  25. Ngo CW, Pong TC, Chin RT (2001) Exploiting image indexing techniques in DCT domain. Pattern Recogn 34(9):1841–1851

    Article  Google Scholar 

  26. Ouahi H, Hajji ME, Afdel K (2014) Secure and image retrieval based on multipurpose watermarking for mammography images database. Int J Comput Appl 90(14):27–32

    Google Scholar 

  27. Ouahi H, Afdel K, Machkour M (2015) Image retrieval based on LBP pyramidal multiresolution using reversible watermarking. Int J Comp Sci Inf Technol 6(2):1888–1893

    Google Scholar 

  28. Phadikar BS, Thakur SS, Maity GK, Phadikar A (2016) A comprehensive evaluation of content-based image retrieval using selected full reference image quality assessment algorithms. In Proc. of 5th international conference on computing, communication and sensor network, pp.167-171.

  29. Phadikar BS, Maity GK, Phadikar A (2016) Full reference image quality assessment: a survey. In: Bhattacharyya S., Sen S., Dutta M., Biswas P., Chattopadhyay H. (eds) industry interactive innovations in science, engineering and technology, lecture notes in networks and systems (LNNS), Springer, 11: 197–208.

  30. Phadikar BS, Phadikar A, Maity GK (2018) Content based image retrieval in DCT compressed domain with MPEG-7 edge descriptor and genetic algorithm. Pattern Anal Applic 21(2):469–489

    Article  MathSciNet  Google Scholar 

  31. Pouri HN, Saryazdi S (2005) Object-based image indexing and retrieval in DCT domain using clustering techniques. In: Proc. of the world academy of science. Eng Technol 3:98–101

    Google Scholar 

  32. Sheikh HR, Bovik AC (2006) Image information and visual quality. IEEE Trans Image Process 15(2):430–444

    Article  Google Scholar 

  33. Singh S, Batra S (2020) An efficient bi-layer content based image retrieval system. Multimed Tools Appl 7:17731–17759

    Article  Google Scholar 

  34. Singh N, Singh K, Sinha AK (2012) A novel approach for content based image retrieval. In: Proc. of the 2nd international conference on computer, communication, control and information technology. Procedia Technology 4:245–250

    Article  Google Scholar 

  35. Tang J, Li Z, Wang M, Zhao R (2015) Neighborhood discriminant hashing for large-scale image retrieval. IEEE Trans Image Process 24(9):2827–2840

    Article  MathSciNet  Google Scholar 

  36. Thawari PB, Janwe NJ (2011) CBIR based on color and texture. Int J Inf Technol Knowl Manag 4(1):129–132

    Google Scholar 

  37. Tyagi V (2017) Content-based image retrieval, 1st edn. Springer, Singapore

    Book  Google Scholar 

  38. Vailaya A, Jain A, Zhang HJ (1998) On image classification: City images vs. landscapes. Pattern Recogn 31(12):1921–1935

    Article  Google Scholar 

  39. Wang Z, Bovik AC (2002) A universal image quality index. IEEE Signal Process Lett 9(3):81–84

    Article  Google Scholar 

  40. Wang Z, Li Q (2001) Information content weighting for perceptual image quality assessment. IEEE Trans Image Process 20(5):1185–1198

    Article  MathSciNet  Google Scholar 

  41. Wang JZ, Li J, Wiederhold G (2001) SIMPLIcity: semantics-sensitive integrated matching for picture libraries. IEEE Trans Patt Anal Mach Intell 23(9):947–963 http://wang.ist.psu.edu/docs/related

    Article  Google Scholar 

  42. Wang Z, Simoncelli EP, Bovik AC (2003) Multi-scale structural similarity for image quality assessment. In. Proc. 37th IEEE Asilomar Conference on Signals, Systems and Computers, Pacific Grove, pp. 1398–1402.

  43. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    Article  Google Scholar 

  44. Xue W, Zhang L, Mou X, Bovik AC (2014) Gradient magnitude similarity deviation: a highly efficient perceptual image quality index. IEEE Trans Image Process 23(2):684–695

    Article  MathSciNet  Google Scholar 

  45. Zhang L, Zhang L, Mou X, Zhang D (2011) FSIM: a feature similarity index for image quality assessment. IEEE Trans Image Process 20(8):2378–2386

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science & Engineering, MCKV Institute of Engineering, Liluah, Howrah, W.B., India

    Baisakhi Sur Phadikar & Subro S. Thakur

  2. Department of Information Technology, MCKV Institute of Engineering, Liluah, Howrah, W.B., India

    Amit Phadikar

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  1. Baisakhi Sur Phadikar
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  2. Amit Phadikar
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  3. Subro S. Thakur
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Correspondence to Amit Phadikar.

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Phadikar, B.S., Phadikar, A. & Thakur, S.S. A comprehensive assessment of content-based image retrieval using selected full reference image quality assessment algorithms. Multimed Tools Appl 80, 15619–15646 (2021). https://doi.org/10.1007/s11042-021-10573-0

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