Generic Graphical User Interface for CBIR Framework

[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 Search in Google Scholar

[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 Search in Google Scholar

[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. Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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. Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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. Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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. Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[23] MathWorks, “Content based image retrieval,” MATLAB Central File Exchange, 2023. [Online]. Available: https://www.mathworks.com/matlabcentral/fileexchange/42008-content-based-image-retrieval Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar