Have a personal or library account? Click to login
Generic Graphical User Interface for CBIR Framework Cover

Generic Graphical User Interface for CBIR Framework

By: Layak Ali  
Open Access
|Jan 2024

References

  1. T. Jaworska, “Fuzzy oriented graphical user interface for content-based image retrieval system,” IFAC Proceedings Volumes, vol. 43, no. 13, pp. 483–488, 2010. https://doi.org/10.3182/20100831-4-FR-2021.00085
  2. V. Tyagi, “Content-based image retrieval techniques: A review,” in Content-Based Image Retrieval. Springer, Singapore, Jan. 2017, pp. 29–48. https://doi.org/10.1007/978-981-10-6759-4_2
  3. Y. Mistry and D. T. Ingole, “Survey on content based image retrieval systems,” International Journal of Innovative Research in Computer and Communication Engineering, vol. 2, pp. 1827–1836, Jan. 2013.
  4. A. Latif et al., “Content-based image retrieval and feature extraction: A comprehensive review,” Mathematical Problems in Engineering, vol. 2019, Aug. 2019, Art. no. 9658350. https://doi.org/10.1155/2019/9658350
  5. P. Saxena and Shefali, “Content based image retrieval system by fusion of color, texture and edge features with SVM classifier and relevance feedback,” International Journal of Research – Granthaalayah, vol. 6, no. 9, pp. 259–273, Sep. 2018. https://doi.org/10.29121/granthaalayah.v6.i9.2018.1230
  6. C. Vasanthanayaki and R. Malini, “An enhanced content based image retrieval system using color features,” International Journal of Engineering and Computer Science, vol. 2, no. 12, Dec. 2013.
  7. A. Arampatzis, K. Zagoris, and S. A. Chatzichristofis, “Dynamic two-stage image retrieval from large multimedia databases,” Information Processing and Management, vol. 49, no. 1, pp. 274–285, Jan. 2013. https://doi.org/10.1016/j.ipm.2012.03.005
  8. P. Vadivel, D Yuvaraj, S. Krishnan, and S.R. Mathusudhanan, “An efficient CBIR system based on color histogram, edge, and texture features,” Concurrency and Computation: Practice and Experience, vol. 31, Oct. 2018, Art. no. e4994. https://doi.org/10.1002/cpe.4994
  9. O. A. B. Penatti, E. Valle, and R. da S. Torres, “Comparative study of global color and texture descriptors for web image retrieval,” Journal of Visual Communication and Image Representation, vol. 23, no. 2, pp. 359–380, Feb. 2012. https://doi.org/10.1016/j.jvcir.2011.11.002
  10. A. Moghimian, M. Mansoorizadeh, and M. H. Dezfoulian, “Content based image retrieval using fusion of multilevel bag of visual words,” SN Applied Sciences, vol. 1, Nov. 2019, Art. no. 1735. https://doi.org/10.1007/s42452-019-1793-5
  11. G. Qiu, J. Morris, and X. Fan, “Visual guided navigation for image retrieval,” Pattern Recognition, vol. 40, no. 6, pp. 1711–1721, Jun. 2007. https://doi.org/10.1016/j.patcog.2006.09.020
  12. S. Hassan, El Mounir, and N. El Maliki, “Combining human visual features for efficient retrieval in faces databases by using a convivial interface,” in The 3rd International Conference on Big Data, Cloud and Applications BDCA’18, Kenitra, Morocco, Apr. 2018.
  13. O. Gambino, L. Rundo, V. Cannella, S. Vitabile, and R. Pirrone, “A framework for data-driven adaptive GUI generation based on DICOM,” Journal of Biomedical Informatics, vol. 88, pp. 37–52, Dec. 2018. https://doi.org/10.1016/j.jbi.2018.10.009
  14. Y. Liu, D. Zhang, G. Lu, and W.-Y. Ma, “A survey of content-based image retrieval with high-level semantics,” Pattern Recognition, vol. 40, no. 1, pp. 262–282, Jan. 2007. https://doi.org/10.1016/j.patcog.2006.04.045
  15. B. Raghunathan and S. T. Acton, “A content based retrieval engine for circuit board inspection,” in Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348), vol. 1, 1999, pp. 104–108.
  16. I. M. Hameed, S. H. Abdulhussain, and B. M. Mahmmod, “Content-based image retrieval: A review of recent trends,” Cogent Engineering, vol. 8, no. 1, Jun. 2021, Art. no. 1927469. https://doi.org/10.1080/23311916.2021.1927469
  17. F. Ali et al., “Content based image retrieval (CBIR) by statistical methods,” Baghdad Science Journal, vol. 17, no. 2(SI), Jun. 2020, Art. no. 0694. https://doi.org/10.21123/bsj.2020.17.2(SI).0694
  18. A. Alzu’bi, A. Amira, and N. Ramzan, “Semantic content based image retrieval: A comprehensive study,” Journal of Visual Communication and Image Representation, vol. 32, pp. 20–54, Oct. 2015. https://doi.org/10.1016/j.jvcir.2015.07.012
  19. A. Tarawneh, C. Celik, A. Hassanat, and D. Chetverikov, “Detailed investigation of deep features with sparse representation and dimensionality reduction in CBIR: A comparative study,” Intelligent Data Analysis, vol. 24, pp. 47–68, Feb. 2020. https://doi.org/10.3233/IDA-184411
  20. N. Ghosh, S. Agrawal, and M. Motwani, “A survey of feature extraction for content-based image retrieval system,” in Proceedings of International Conference on Recent Advancement on Computer and Communication. Lecture Notes in Networks and Systems, B. Tiwari, V. Tiwari, K. Das, D. Mishra, and J. Bansal, Eds., vol 34. Springer, Singapore, Jan. 2018, pp. 305–313. https://doi.org/10.1007/978-981-10-8198-9_32
  21. T. Deserno, M. G¨uld, B. Plodowski, K. Spitzer, B. Wein, H. Schubert, H. Ney, and T. Seidl, “Extended query refinement for medical image retrieval,” Journal of Digital Imaging: The Official Journal of the Society for Computer Applications in Radiology, vol. 21, pp. 280–289, Jun. 2007. https://doi.org/10.1007/s10278-007-9037-4
  22. X.-Y. Wang, L.-L. Liang, Y.-W. Li, and H.-Y. Yang, “Image retrieval based on exponent moments descriptor and localized angular phase histogram,” Multimedia Tools and Applications, vol. 76, pp. 7633–7659, Mar. 2017. https://doi.org/10.1007/s11042-016-3416-0
  23. MathWorks, “Content based image retrieval,” MATLAB Central File Exchange, 2023. [Online]. Available: https://www.mathworks.com/matlabcentral/fileexchange/42008-content-based-image-retrieval
  24. M. K. Chigateri and S. Sonoli, “CBIR algorithm development using RGB histogram-based block contour method to improve the retrieval performance,” Materials Today: Proceedings, vol. 81, no. 2, pp. 314–321, 2023. https://doi.org/10.1016/j.matpr.2021.03.198
  25. Y. Mistry, M. D. Ingole, and D. T. Ingole, “Content based image retrieval using hybrid features and various distance metric,” Journal of Electrical Systems and Information Technology, vol. 5, no. 3, pp. 874–888, Dec. 2018. https://doi.org/10.1016/j.jesit.2016.12.009
  26. P A. Vikhar and P. P. Karde, “Content based image retrieval (CBIR) system using threshold based color layout descriptor (CLD) and edge histogram descriptor (EHD),” International Journal of Computer Applications, vol. 179, no. 41, pp. 39–43, May 2018. https://doi.org/10.5120/ijca2018916985
  27. Md. F. Sadique and S. M. R. Haque, “Content-based image retrieval using color layout descriptor, gray-level co-occurrence matrix and k-nearest neighbors,” International Journal of Information Technology and Computer Science, vol. 12, no. 3, pp. 19–25, Jun. 2020. https://doi.org/10.5815/ijitcs.2020.03.03
  28. C.-H. Lin, C.-C. Chen, H.-L. Lee, and J.-R. Liao, “Fast k-means algorithm based on a level histogram for image retrieval,” Expert Systems with Applications, vol. 41, no. 7, pp. 3276–3283, Jun. 2014. https://doi.org/10.1016/j.eswa.2013.11.017
  29. J. Li and J. Z. Wang, “Automatic linguistic indexing of pictures by a statistical modeling approach,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 9, pp. 1075–1088, Sep. 2003. https://doi.org/10.1109/TPAMI.2003.1227984
  30. J. Z. Wang, J. Li, and G. Wiederhold, “Simplicity: semantics-sensitive integrated matching for picture libraries,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 9, pp. 947–963, Sep. 2001. https://doi.org/10.1109/34.955109
DOI: https://doi.org/10.2478/acss-2023-0020 | Journal eISSN: 2255-8691 | Journal ISSN: 2255-8683
Language: English
Page range: 203 - 209
Published on: Jan 29, 2024
Published by: Riga Technical University
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2024 Layak Ali, published by Riga Technical University
This work is licensed under the Creative Commons Attribution 4.0 License.