Content-based image retrieval: A review of recent trends

References

• Abdul-Hadi, A. M., Abdulhussain, S. H., Mahmmod, B. M., & Pham, D. (2020, January). On the computational aspects of Charlier polynomials. Cogent Engineering, 7(1), 1763553. https://doi.org/10.1080/23311916.2020.1763553
   Web of Science ®Google Scholar
• Abdulhussain, S. H., & Mahmmod, B. M. (2021, April). Fast and efficient recursive algorithm of Meixner polynomials. Journal of Real-Time Image Processing. https://doi.org/10.1007/s11554-021-01093-z
   Google Scholar
• Abdulhussain, S. H., Mahmmod, B. M., Naser, M. A., Alsabah, M. Q., Ali, R., & Al-Haddad, S. A. R. (2021, March). A robust handwritten numeral recognition using hybrid orthogonal polynomials and moments. Sensors, 21(6), 1999. https://doi.org/10.3390/s21061999
   PubMedGoogle Scholar
• Abdulhussain, S. H., Ramli, A. R., Al-Haddad, S. A. R., Mahmmod, B. M., & Jassim, W. A. (2017). On computational aspects of tchebichef polynomials for higher polynomial order. IEEE Access, 5(1), 2470–37. https://doi.org/10.1109/ACCESS.2017.2669218
   Google Scholar
• Abdulhussain, S. H., Ramli, A. R., Hussain, A. J., Mahmmod, B. M., & Jassim, W. A., “Orthogonal polynomial embedded image kernel,” in Proceedings of the International Conference on Information and Communication Technology - ICICT ’19 Baghdad, Iraq (pp. 215–221).
   Google Scholar
• Abdulhussain, S. H., Ramli, A. R., Mahmmod, B. M., Saripan, M. I., Al-Haddad, S. A. R., & Jassim, W. A. (2019, May). A new hybrid form of Krawtchouk and Tchebichef Polynomials: Design and application. Journal of Mathematical Imaging and Vision, 61(4), 555–570. https://doi.org/10.1007/s10851-018-0863-4
   Web of Science ®Google Scholar
• Agarwal, M., Singhal, A., & Lall, B. (2019, November). Multi-channel local ternary pattern for content-based image retrieval. Pattern Analysis and Applications, 22(4), 1585–1596. https://doi.org/10.1007/s10044-019-00787-2
   Web of Science ®Google Scholar
• Ahmed, K. T., Ummesafi, S., & Iqbal, A. (2019, November). Content based image retrieval using image features information fusion. Information Fusion, 51, 76–99. https://doi.org/10.1016/j.inffus.2018.11.004
   Web of Science ®Google Scholar
• Ali, F., and Hashem, A. (2020, June). Content Based Image Retrieval (CBIR) by statistical methods. Baghdad Science Journal, 17 (2(SI)), 694. https://doi.org/10.21123/bsj.2020.17.2(SI).0694
   Web of Science ®Google Scholar
• Alsmadi, M. K. (2020, April). Content-based image retrieval using color, shape and texture descriptors and features. Arabian Journal for Science and Engineering, 45(4), 3317–3330. https://doi.org/10.1007/s13369-020-04384-y
   Web of Science ®Google Scholar
• Alzu’bi, A., Amira, A., & Ramzan, N. (2015, October). Semantic content-based image retrieval: A comprehensive study. Journal of Visual Communication and Image Representation, 32, 20–54. https://doi.org/10.1016/j.jvcir.2015.07.012
   Web of Science ®Google Scholar
• Alzu’bi, A., Amira, A., & Ramzan, N. (2017, August). Content-based image retrieval with compact deep convolutional features. Neurocomputing, 249, 95–105. https://doi.org/10.1016/j.neucom.2017.03.072
   Web of Science ®Google Scholar
• Arevalillo-Herráez, M., Domingo, J., & Ferri, F. J. (2008, December). Combining similarity measures in content-based image retrieval. Pattern Recognition Letters, 29(16), 2174–2181. https://doi.org/10.1016/j.patrec.2008.08.003
   Web of Science ®Google Scholar
• Arthur, D., & Vassilvitskii, S. (2007, January). K-means++: The advantages of careful seeding. Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms, 07-09, 1027–1035. https://doi.org/10.5555/1283383.1283494
   Google Scholar
• Ashraf, R., Ahmedm, M., Jabbar, S., Khalid, S., Ahmad, A., Din, S. & Jeon, G. (2018, March). Content based image retrieval by using color descriptor and discrete Wavelet transform. Journal of Medical Systems, 42 (3), 44. https://doi.org/10.1007/s10916-017-0880-7
   PubMed Web of Science ®Google Scholar
• Ashraf, R., Ahmed, M., Ahmad, U., Habib, M. A., Jabbar, S., & Naseer, K. (2020, April). MDCBIR-MF: Multimedia data for content-based image retrieval by using multiple features. Multimedia Tools and Applications, 79(13–14), 8553–8579. https://doi.org/10.1007/s11042-018-5961-1
   Web of Science ®Google Scholar
• Ashraf, R., Bashir, K., Irtaza, A., & Mahmood, M. (2015, May). Content based image retrieval using embedded neural networks with bandletized regions. Entropy, 17(6), 3552–3580. https://doi.org/10.3390/e17063552
   Web of Science ®Google Scholar
• Bai, C., Chen, J., Huang, L., Kpalma, K., & Chen, S. (2018, January). Saliency-based multi-feature modeling for semantic image retrieval. Journal of Visual Communication and Image Representation, 50, 199–204. https://doi.org/10.1016/j.jvcir.2017.11.021
   Web of Science ®Google Scholar
• Baig, F., Mehmood, Z., Rashid, M., Javid, M. A., Rehman, A., Saba, T., & Adnan, A. (2020, March). Boosting the performance of the BoVW model using SURF–CoHOG-based sparse features with relevance feedback for CBIR. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 44(1), 99–118. https://doi.org/10.1007/s40998-019-00237-z
   Web of Science ®Google Scholar
• Bala, A., & Kaur, T. (2016, March). Local texton XOR patterns: A new feature descriptor for content-based image retrieval. International Journal of Engineering Technology and Scientific Innovation, 19(1), 101–112. https://doi.org/10.1016/j.jestch.2015.06.008
   Google Scholar
• Banerjee, I., Kurtz, C., Devorah, A. E., Do, B., Rubin, D. L., & Beaulieu, C. F. (2018, August). Relevance feedback for enhancing content based image retrieval and automatic prediction of semantic image features: Application to bone tumor radiographs. Journal of Biomedical Informatics, 84, 123–135. https://doi.org/10.1016/j.jbi.2018.07.002
   PubMed Web of Science ®Google Scholar
• Bansal, M., Kumar, M., & Kumar, M. (2020, February). 2D object recognition techniques: State-of-the-art work. Archives of Computational Methods in Engineering, 28(3), 1147-1161. https://doi.org/10.1007/s11831-020-09409-1
   Google Scholar
• Basheer, I., & Hajmeer, M. (2000, December). Artificial neural networks: Fundamentals, computing, design, and application. Journal of Microbiological Methods, 43(1), 3–31. https://doi.org/10.1016/S0167-7012(00)00201-3
   PubMed Web of Science ®Google Scholar
• Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008, June). Speeded-Up Robust Features (SURF). Computer Vision and Image Understanding, 110(3), 346–359. https://doi.org/10.1016/j.cviu.2007.09.014
   Web of Science ®Google Scholar
• Bibi, R., Mehmood, Z., Yousaf, R. M., Saba, T., Sardaraz, M., & Rehman, A. (2020a, April). Query-by-visual-search: Multimodal framework for content-based image retrieval. Journal of Ambient Intelligence and Humanized Computing, 11(11), 5629-5648. https://doi.org/10.1007/s12652-020-01923-1
   Google Scholar
• Bibi, R., Mehmood, Z., Yousaf, R. M., Saba, T., Sardaraz, M., & Rehman, A.“SIPI-USC Brodatz texture image database.” [ Online]. University of Southern California. Available: http://sipi.usc.edu/database/database.php?volume=textures. [Accessed: 01-Jan-2020b].
   Google Scholar
• Cha, S.-H. (2007). Comprehensive Survey on Distance Similarity measures between probability density functions. International Journal of Mathematics Model Methods Applied Sciiences, 1(4). https://www.naun.org/main/NAUN/ijmmas/mmmas-49.pdf
   Google Scholar
• Chaudhry, S., & Chandra, R. (2016, October). Unconstrained face detection from a mobile source using convolutional neural networks. Lecture Notes in Computer Science, 9948, 567–576. Including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics. https://link.springer.com/chapter/10.1007%2F978-3-319-46672-9_63
   Google Scholar
• Chun, Y. D., Kim, N. C., & Jang, I. H. (2008, October). Content-based image retrieval using multiresolution color and texture features. IEEE Transactions on Multimedia, 10(6), 1073–1084. https://doi.org/10.1109/TMM.2008.2001357
   Web of Science ®Google Scholar
• Ciocca, G., & Schettini, R. (1999, September). A relevance feedback mechanism for content-based image retrieval. Information Processing & Management, 35(5), 605–632. https://doi.org/10.1016/S0306-4573(99)00021-7
   Web of Science ®Google Scholar
• Cortes, C., & Vapnik, V. (1995, September). Support-vector networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1007/BF00994018
   Web of Science ®Google Scholar
• Cross, G. R., & Jain, A. K. (1983, January). Markov random field texture models. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-5(1), 25–39. https://doi.org/10.1109/TPAMI.1983.4767341
   Web of Science ®Google Scholar
• Cula, O. G., & Dana, K. J. (2004). 3D texture recognition using bidirectional feature histograms. International Journal of Computer Vision, 59(1), 33–60. https://doi.org/10.1023/B:VISI.0000020670.05764.55
   Web of Science ®Google Scholar
• Dalal, N., & Triggs, B., “Histograms of oriented gradients for human detection,” In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 2005. San Diego, CA.
   Google Scholar
• Das, R., Thepade, S., Bhattacharya, S., & Ghosh, S. (2016). Retrieval architecture with classified query for content based image recognition. Applied Computer Intellegent Software Computers, 2016, 1–9. doi: 10.1155/2016/1861247
   Google Scholar
• Datta, R., Joshi, D., Li, J., & Wang, J. Z. (2008, April). Image retrieval. ACM Computing Surveys, 40(2), 1–60. https://doi.org/10.1145/1348246.1348248
   Web of Science ®Google Scholar
• Duanmu, X., “Image retrieval using color moment invariant,” in 2010 Seventh International Conference on Information Technology: New Generations, 2010, pp. 200–203. Las Vegas, Nevada.
   Google Scholar
• ElAdel, A., Ejbali, R., Zaied, M., & Ben Amar, C. (2016, August). A hybrid approach for content-based image retrieval based on fast beta wavelet network and fuzzy decision support system. Machine Vision and Applications, 27(6), 781–799. https://doi.org/10.1007/s00138-016-0789-z
   Web of Science ®Google Scholar
• Fei-Fei, L., Fergus, R., & Perona, P. (2007, April). Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories. Computer Vision and Image Understanding, 106(1), 59–70. https://doi.org/10.1016/j.cviu.2005.09.012
   Web of Science ®Google Scholar
• Flickner, M., Sawhney, H., Niblack, W., Ashley, J., Huang, Q., Dom, B., Gorkani, M., .. Yanker, P. (1995). Query by image and video content: The QBIC system. Computer (Long. Beach. Calif), 28(9), 23–32. http://ieeexplore.ieee.org/document/410146/
   Web of Science ®Google Scholar
• Forssén, P. E., & Lowe, D. G. (2007). Shape descriptors for maximally stable extremal regions. Proceedings / IEEE International Conference on Computer Vision, 0–7. DOI: 10.1109/ICCV.2007.4409025
   Google Scholar
• Fowler, B. (2000, February). A Sociological Analysis of the Satanic Verses Affair. Theory, Culture & Society, 17(1), 39–61. https://doi.org/10.1177/02632760022050997
   Web of Science ®Google Scholar
• Garg, M., & Dhiman, G. (2020, June). A novel content-based image retrieval approach for classification using GLCM features and texture fused LBP variants. Neural Computing & Applications, 33(4), 1311-1328. DOI: 10.1007/s00521-020-05017-z
   Google Scholar
• Ghosh, N., Agrawal, S., & Motwani, M. 2018. A survey of feature extraction for content-based image retrieval system. Lecture notes in networks and systems (Vol. 34 305–313).Springer. https://doi.org/10.1007/978-981-10-8198-9_32
   Google Scholar
• Ghrabat, M. J. J., Ma, G., Maolood, I. Y., Alresheedi, S. S., & Abduljabbar, Z. A. (2019, December). An effective image retrieval based on optimized genetic algorithm utilized a novel SVM-based convolutional neural network classifier. Human-centric Computing and Information Sciences, 9(1), 31. https://doi.org/10.1186/s13673-019-0191-8
   Web of Science ®Google Scholar
• Gogul, I., & Kumar, V. S., “Flower species recognition system using convolution neural networks and transfer learning,” in 2017 Fourth International Conference on Signal Processing, Communication and Networking (ICSCN), 2017, no. March, pp. 1–6. Chennai, India.
   Google Scholar
• Griffin, G., Holub, A., & Perona, P. (2007). Caltech-256 object category dataset. Caltech mimeo, 11(1), 20. https://authors.library.caltech.edu/7694/
   Google Scholar
• Guo, Z., Zhang, L., & Zhang, D. (2010, March). Rotation invariant texture classification using LBP variance (LBPV) with global matching. Pattern Recognition, 43(3), 706–719. https://doi.org/10.1016/j.patcog.2009.08.017
   Web of Science ®Google Scholar
• Haji, M. S., Alkawaz, M. H., Rehman, A., & Saba, T. (2019). Content-based image retrieval: A deep look at features prospectus. International Journal of Computational Vision and Robotics, 9(1), 14. https://doi.org/10.1504/IJCVR.2019.098004
   Google Scholar
• Halawani, A. H., Teynor, A., Setia, L., Brunner, G., & Retrieval, C. I. (2006, January). Fundamentals and Applications of Image Retrieval : An Overview. Image (Rochester, N.Y.), 14–23.
   Google Scholar
• Harris, C., & Stephens, M., “A combined corner and edge detector,” In Procedings of the Alvey Vision Conference 1988, 1988, pp. 23.1–23.6. Manchester, UK.
   Google Scholar
• Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136: A K-means clustering algorithm. Applied Statistics, 28(1), 100. https://doi.org/10.2307/2346830
   Google Scholar
• Hawlick, R. M. (2017). Statistical and structural approaches to texture. Advances in Intelligent Systems and Computing, 459(5), 1–644. doi: 10.1109/PROC.1979.11328
   Google Scholar
• Howarth, P., & Rüger, S. (2005). Fractional distance measures for content-based image retrieval. 447–456. doi: 10.1007/978-3-540-31865-1_32
   Google Scholar
• Huang, J., Kumar, S. R., Mitra, M., Zhu, W.-J., & Zabih, R., “Image indexing using color correlograms,” in Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on, 1997, pp. 762–768. San Juan, PR.
   Google Scholar
• Idan, Z. N., Abdulhussain, S. H., & Al-Haddad, S. A. R. (2020). A new separable moments based on tchebichef-krawtchouk polynomials. IEEE Access, 8, 41013–41025. https://doi.org/10.1109/ACCESS.2020.2977305
   Web of Science ®Google Scholar
• Jabeen, S., Mehmood, Z., Mahmood, T., Saba, T., Rehman, A., Mahmood, M. T., & Rubin, D. L. (2018, April). An effective content-based image retrieval technique for image visuals representation based on the bag-of-visual-words model. PLoS One, 13(4), e0194526. https://doi.org/10.1371/journal.pone.0194526
   PubMed Web of Science ®Google Scholar
• Jacobs, C. E., Finkelstein, A., & Salesin, D. H., “Fast multiresolution image querying,” in Proceedings of the 22nd annual conference on Computer graphics and interactive techniques - SIGGRAPH ’95, New York, USA, 1995, pp. 277–286.
   Google Scholar
• Jain, M., Jégou, H., & Gros, P., “Asymmetric hamming embedding,” in Proceedings of the 19th ACM international conference on Multimedia - MM ’11, New York, USA, 2011, no. May, p. 1441.
   Google Scholar
• Jenni, K., Mandala, S., & Sunar, M. S. (2015). Content based image retrieval using colour strings comparison. Procedia Computer Science, 50, 374–379. https://doi.org/10.1016/j.procs.2015.04.032
   Google Scholar
• Kokare, M., Chatterji, B. N., & Biswas, P. K. (2002, May). A survey on current content based image retrieval methods. IETE Journal of Research, 48(3–4), 261–271. https://doi.org/10.1080/03772063.2002.11416285
   Web of Science ®Google Scholar
• Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017, May). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90. https://doi.org/10.1145/3065386
   Web of Science ®Google Scholar
• Lance, G. N., & Williams, W. T. (1966, May). Computer programs for hierarchical polythetic classification (‘Similarity Analyses’). The Computer Journal, 9(1), 60–64. https://doi.org/10.1093/comjnl/9.1.60
   Web of Science ®Google Scholar
• Lance, G. N., & Williams, W. T. (1967). Mixed-data classificatory programs i - agglomerative systems. Australian Computer Journal, 1(1), 15–20. https://www.semanticscholar.org/paper/Mixed-Data-Classificatory-Programs-I-Agglomerative-Lance-Williams/4072b6353dd761ba0c219a4b860073f08bb3db27
   Google Scholar
• Latif, A., Rasheed, A., Sajid, U., Ahmed, J., Ali, N., Ratyal, N. I., Zafar, B., Dar, S. H., Sajid, M., & Khalil, T. (2019, August). Content-based image retrieval and feature extraction: a comprehensive review. Mathematical Problems in Engineering, 1–21. https://doi.org/10.1155/2019/9658350
   Google Scholar
• Lazebnik, S., Schmid, C., & Ponce, J., “Beyond bags of features: spatial pyramid matching for recognizing natural scene categories,” in 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR’06), New York, USA, 2006, vol. 2, pp. 2169–2178.
   Google Scholar
• Leutenegger, S., Chli, M., & Siegwart, R. Y., “BRISK: Binary Robust invariant scalable keypoints,” in 2011 International Conference on Computer Vision, Barcelona, Spain, 2011, pp. 2548–2555.
   Google Scholar
• Li, H., Qin, J., Xiang, X., Pan, L., Ma, W., & Xiong, N. N. (2018). An efficient image matching algorithm based on adaptive threshold and RANSAC. IEEE Access, 6, 66963–66971. https://doi.org/10.1109/ACCESS.2018.2878147
   Web of Science ®Google Scholar
• Ling, H., & Jacobs, D. W. (2007). Shape classification using the inner-distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(2), 286–299. https://doi.org/10.1109/TPAMI.2007.41
   PubMed Web of Science ®Google Scholar
• Liu, P., Guo, J.-M., Wu, C.-Y., & Cai, D. (2017, December). Fusion of deep learning and compressed domain features for content-based image retrieval. The Computer Journal, 26(12), 5706–5717. https://doi.org/10.1109/TIP.2017.2736343
   Google Scholar
• Liu, Y., Zhang, D., Lu, G., & Ma, W.-Y. (2007). A survey of content-based image retrieval with high-level semantics. Pattern Recognition, 40(1), 262–282. https://doi.org/10.1016/j.patcog.2006.04.045
   Web of Science ®Google Scholar
• Low, D. G. (2004). Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis, 60(2), 91–110. https://doi.org/10.1023/B:VISI.0000029664.99615.94
   Web of Science ®Google Scholar
• Mahmmod, B. M., Abdul-Hadi, A. M., Abdulhussain, S. H., & Hussien, A. (2020, August). On computational aspects of Krawtchouk polynomials for high orders. Journal of Imaging, 6(8), 81. https://doi.org/10.3390/jimaging6080081
   PubMed Web of Science ®Google Scholar
• Makhoul, J., Kubala, F., Schwartz, R., & Weischedel, R. (1999, February). Performance measures for information extraction. In Proc. DARPA broadcast news work. (pp. 249–252).
   Google Scholar
• Manjunath, B. S., Ohm, J.-R., Vasudevan, V. V., & Yamada, A. (2001, June). Color and texture descriptors. IEEE Transactions on Circuits and Systems for Video Technology, 11(6), 703–715. https://doi.org/10.1109/76.927424
   Web of Science ®Google Scholar
• Markowska-Kaczmar, U., & Kwaśnicka, H. (2018). Deep learning—a new era in bridging the semantic gap. Intelligent Systems Reference Library, 145, 123–159. doi: 10.1007/978-3-319-73891-8_7
   Google Scholar
• Martin, D. R., Fowlkes, C. C., & Malik, J. (2004). Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(5), 530–549. https://doi.org/10.1109/TPAMI.2004.1273918
   PubMed Web of Science ®Google Scholar
• Marxsen, S., Hilbert, D., Laubenbacher, R., Sturmfels, B., David, H., & Laubenbacher, R. C. (1993). Theory of algebraic invariants. Cambridge University Press.
   Google Scholar
• Mehmood, Z., Abbas, F., Mahmood, T., Javid, M. A., Rehman, A., & Nawaz, T. (2018, December). Content-based image retrieval based on visual words fusion versus features fusion of local and global features. Arabian Journal for Science and Engineering, 43(12), 7265–7284. https://doi.org/10.1007/s13369-018-3062-0
   Web of Science ®Google Scholar
• Mehmood, Z., Anwar, S. M., Ali, N., Habib, H. A., & Rashid, M. (2016). A novel image retrieval based on a combination of local and global histograms of visual words. Mathematical Problems in Engineering, 2016, 1–12. https://doi.org/10.1155/2016/8217250
   Web of Science ®Google Scholar
• Mittal, U., Srivastava, S., & Chawla, P., “Review of different techniques for object detection using deep learning,” in Proceedings of the Third International Conference on Advanced Informatics for Computing Research - ICAICR ’19, New York, USA, 2019, pp. 1–8.
   Google Scholar
• Montazer, G. A., & Giveki, D. (2015, September). Content based image retrieval system using clustered scale invariant feature transforms. Optik (Stuttg), 126(18), 1695–1699. https://doi.org/10.1016/j.ijleo.2015.05.002
   Web of Science ®Google Scholar
• Naeem, M., Ashraf, R., Ali, N., Ahmad, M., & Habib, M. A., “Bottom up approach for better requirements elicitation,” in Proceedings of the International Conference on Future Networks and Distributed Systems - ICFNDS ’17, New York, USA, 2017, 1305, pp. 1–4.
   Google Scholar
• Nazir, A., Ashraf, R., Hamdani, T., & Ali, N., “Content based image retrieval system by using HSV color histogram, discrete wavelet transform and edge histogram descriptor,” in 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), Sukkur, Pakistan, 2018, January, pp. 1–6.
   Google Scholar
• Ojala, T., Pietikainen, M., & Maenpaa, T. (2002, July). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 971–987. https://doi.org/10.1109/TPAMI.2002.1017623
   Web of Science ®Google Scholar
• Oliva, A., & Torralba, A. (2001). Modeling the shape of the scene: A holistic representation of the spatial envelope. International Journal of Computer Vision, 42(3), 145–175. https://doi.org/10.1023/A:1011139631724
   Web of Science ®Google Scholar
• Otsu, N. (1979, January). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics. Systems, 9(1), 62–66. https://doi.org/10.1109/TSMC.1979.4310076
   Web of Science ®Google Scholar
• Öztürk, Ş. (2020, December). Stacked auto-encoder based tagging with deep features for content-based medical image retrieval. Expert Systems with Applications, 161, 113693. https://doi.org/10.1016/j.eswa.2020.113693
   Web of Science ®Google Scholar
• Patil, A., & Rane, M. (2021). Convolutional neural networks: an overview and its applications in pattern recognition. In Smart Innovation, Systems and Technologies, 195(Insights into Imaging), 21–30. DOI: 10.1007/978-981-15-7078-0_3
   Google Scholar
• Pavithra, L. K., & Sharmila, T. S. (2018, August). An efficient framework for image retrieval using color, texture and edge features. Computers & Electrical Engineering, 70, 580–593. https://doi.org/10.1016/j.compeleceng.2017.08.030
   Web of Science ®Google Scholar
• Pavithra, L. K., & Sree Sharmila, T. (2019, December). An efficient seed points selection approach in dominant color descriptors (DCD). Cluster Computing, 22(4), 1225–1240. https://doi.org/10.1007/s10586-019-02907-3
   Web of Science ®Google Scholar
• Pele, O., & Werman, M. (2010). The quadratic-chi histogram distance family. Lecture Notes in Computer Science, 6312 (2), 749–762. Including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics. DOI: 10.1007/978-3-642-15552-9_54
   Google Scholar
• Perronnin, F., Douze, M., Pe, P., Schmid, C., & Sa, J. (2012). Aggregating local image descriptors into compact codes. Analysis, 34(9), 1704–1716. https://doi.org/10.1109/TPAMI.2011.235
   Web of Science ®Google Scholar
• Phadikar, B. S., Phadikar, A., & Maity, G. K. (2018, May). Content-based image retrieval in DCT compressed domain with MPEG-7 edge descriptor and genetic algorithm. Pattern Analysis and Applications, 21(2), 469–489. https://doi.org/10.1007/s10044-016-0589-0
   Web of Science ®Google Scholar
• Piras, L., & Giacinto, G. (2017, September). Information fusion in content based image retrieval: A comprehensive overview. Information Fusion, 37, 50–60. https://doi.org/10.1016/j.inffus.2017.01.003
   Web of Science ®Google Scholar
• Ponomarev, A., Nalamwar, H. S., Babakov, I., Parkhi, C. S., & Buddhawar, G. (2016, February). Content-based image retrieval using color, texture and shape features. Key Engineering Materials, 685, 872–876. https://doi.org/10.4028/www.scientific.net/KEM.685.872
   Google Scholar
• Qin, J., Li, H., Xiang, X., Tan, Y., Pan, W., Ma, W., & Xiong, N. N. (2019). An encrypted image retrieval method based on harris corner optimization and LSH in cloud computing. IEEE Access, 7, 24626–24633. https://doi.org/10.1109/ACCESS.2019.2894673
   Web of Science ®Google Scholar
• Qiu, G. (2003, January). Color image indexing using BTC. IEEE Transactions on Image Processing, 12(1), 93–101. https://doi.org/10.1109/TIP.2002.807356
   PubMed Web of Science ®Google Scholar
• Raghunathan, B., & Acton, S. T., “A content based retrieval engine for circuit board inspection,” in Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348), vol. 1, pp. 104–108 1999. Kobe, Japan.
   Google Scholar
• Rana, S. P., Dey, M., & Siarry, P. (2019, January). Boosting content based image retrieval performance through integration of parametric & nonparametric approaches. Journal of Visual Communication and Image Representation, 58(3), 205–219. https://doi.org/10.1016/j.jvcir.2018.11.015
   Web of Science ®Google Scholar
• Raza, A., Dawood, H., Dawood, H., Shabbir, S., Mehboob, R., & Banjar, A. (2018). Correlated primary visual texton histogram features for content base image retrieval. IEEE Access, 6, 46595–46616. https://doi.org/10.1109/ACCESS.2018.2866091
   Web of Science ®Google Scholar
• Raza, A., Nawaz, T., Dawood, H., & Dawood, H. (2019). Square texton histogram features for image retrieval. Multimedia Tools and Applications, 78(3), 2719–2746. https://doi.org/10.1007/s11042-018-5795-x
   Web of Science ®Google Scholar
• Rosten, E., Porter, R., & Drummond, T., “Faster and better: A machine learning approach to corner detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 1, pp. 105–119, January. 2010. Hannover, Germany. https://doi.org/10.1109/TPAMI.2008.275
   Google Scholar
• Rui, H. S., Ruger, D. S., Liu, H., & Huang, Z., “Dissimilarity measures for content-based image retrieval,” In 2008 IEEE International Conference on Multimedia and Expo, Hannover, Germany, 2008, pp. 1365–1368.
   Google Scholar
• Sajjad, M., Ullah, A., Ahmad, J., Abbas, N., Rho, S., & Baik, S. W. (2018, February). Integrating salient colors with rotational invariant texture features for image representation in retrieval systems. Multimedia Tools and Applications, 77(4), 4769–4789. https://doi.org/10.1007/s11042-017-5010-5
   Web of Science ®Google Scholar
• Salve, S. G., & Jondhale, K. C. (2010). Shape matching and object recognition using shape contexts. Proceedings - 2010 3rd IEEE International Conference Computer Science Information Technology ICCSIT 2010, 9, 471–474. DOI: 10.1109/ICCSIT.2010.5565098
   Google Scholar
• Sammut, C., & Webb, G. I. (Eds.). (2017). Encyclopedia of machine learning and data mining. Springer US.
   Google Scholar
• Sánchez, J., Monzón, N., & Salgado, A. (2018, October). An analysis and implementation of the harris corner detector. Image Processing Line, 8(9), 305–328. DOI: 10.5201/ipol.2018.229
   Google Scholar
• Sarwar, A., Mehmood, Z., Saba, T., Qazi, K. A., Adnan, A., & Jamal, H. (2019, February). A novel method for content-based image retrieval to improve the effectiveness of the bag-of-words model using a support vector machine. Journal of Information Science, 45(1), 117–135. https://doi.org/10.1177/0165551518782825
   Web of Science ®Google Scholar
• Sergyan, S., “Color histogram features based image classification in content-based image retrieval systems,” in 2008 6th International Symposium on Applied Machine Intelligence and Informatics, Herlany, Slovakia, 2008, pp. 221–224.
   Google Scholar
• Sezavar, A., Farsi, H., & Mohamadzadeh, S. (2019, August). Content-based image retrieval by combining convolutional neural networks and sparse representation. Multimedia Tools and Applications, 78(15), 20895–20912. https://doi.org/10.1007/s11042-019-7321-1
   Web of Science ®Google Scholar
• Shamna, P., Govindan, V. K., & Abdul Nazeer, K. A. (2019, March). Content based medical image retrieval using topic and location model. Journal of Biomedical Informatics, 91, 103112. https://doi.org/10.1016/j.jbi.2019.103112
   PubMed Web of Science ®Google Scholar
• Sharif, U., Mehmood, Z., Mahmood, T., Javid, M. A., Rehman, A., & Saba, T. (2019, August). Scene analysis and search using local features and support vector machine for effective content-based image retrieval. Artificial Intelligence Review, 52(2), 901–925. https://doi.org/10.1007/s10462-018-9636-0
   Web of Science ®Google Scholar
• Sharmi, N., Mohamed Shameem, P., & Parvathy, R. (2018). Content-based image retrieval using fast machine learning approach in cloud computing. In Communications in computer and information science (Vol. 837, pp. 434–444). Elsevier: Springer Singapore.
   Google Scholar
• Shrivastava, N., & Tyagi, V. (2015, August). An efficient technique for retrieval of color images in large databases. Computers & Electrical Engineering, 46, 314–327. https://doi.org/10.1016/j.compeleceng.2014.11.009
   Web of Science ®Google Scholar
• Shrivastava, N., & Tyagi, V. (2017, December). Corrigendum to “Content based image retrieval based on relative locations of multiple regions of interest using selective regions matching” [Information Sciences 259 (2014) 212–224]. Information Sciences, 421, 273. https://doi.org/10.1016/j.ins.2017.09.017
   Web of Science ®Google Scholar
• Simonyan, K., & Zisserman, A. (2014, September). Very deep convolutional networks for large-scale image recognition. 3rd International Conference Learning Represent ICLR 2015 - Conference Track Proceedings, 1–14.
   Google Scholar
• Song, W., Zhang, Y., Liu, F., Chai, Z., Ding, F., Qian, X., & Park, S. C. (2018, April). Taking advantage of multi-regions-based diagonal texture structure descriptor for image retrieval. Expert Systems with Applications, 96, 347–357. https://doi.org/10.1016/j.eswa.2017.12.006
   Web of Science ®Google Scholar
• Srivastava, P., & Khare, A. (2017, January). Integration of wavelet transform, local binary patterns and moments for content-based image retrieval. Journal of Visual Communication and Image Representation, 42, 78–103. https://doi.org/10.1016/j.jvcir.2016.11.008
   Web of Science ®Google Scholar
• Su, J.-H., Huang, W.-J., Yu, P. S., & Tseng, V. S. (2011, March). Efficient relevance feedback for content-based image retrieval by mining user navigation patterns. IEEE Transactions on Knowledge and Data Engineering, 23(3), 360–372. https://doi.org/10.1109/TKDE.2010.124
   Web of Science ®Google Scholar
• Suk, T., & Flusser, J. (2011, September). Affine moment invariants generated by graph method. Pattern Recognition, 40(2), 2047–2056. https://doi.org/10.1016/j.patcog.2010.05.015
   Google Scholar
• Swain, M. J., & Ballard, D. H. (1991). Color indexing. International Journal of Computer Vision, 7(1), 11–32. https://doi.org/10.1007/BF00130487
   Web of Science ®Google Scholar
• Tadi Bani, N., & Fekri-Ershad, S. (2019, August). Content-based image retrieval based on combination of texture and colour information extracted in spatial and frequency domains. Electronic Library, 37(4), 650–666. https://doi.org/10.1108/EL-03-2019-0067
   Web of Science ®Google Scholar
• Tao, D., Tang, X., Xuelong, L., & Rui, Y. (2006, August). Direct kernel biased discriminant analysis: A new content-based image retrieval relevance feedback algorithm. IEEE Transactions on Multimedia, 8(4), 716–727. https://doi.org/10.1109/TMM.2005.861375
   Web of Science ®Google Scholar
• Tarawneh, A. S., Celik, C., Hassanat, A. B., & Chetverikov, D. (2020, February). Detailed investigation of deep features with sparse representation and dimensionality reduction in CBIR: A comparative study. Intelligent Data Analysis, 24(1), 47–68. https://doi.org/10.3233/IDA-184411
   Web of Science ®Google Scholar
• Thusnavis Bella, M. I., & Vasuki, A. (2019, May). An efficient image retrieval framework using fused information feature. Computers & Electrical Engineering, 75, 46–60. https://doi.org/10.1016/j.compeleceng.2019.01.022
   Web of Science ®Google Scholar
• Tian, D. (2018). Support vector machine for content-based image retrieval: A comprehensive overview. Journal Information Hiding Multimedia Signal Processing, 9(6), 1464–1478. http://bit.kuas.edu.tw/~jihmsp/2018/vol9/Number6/JIH-MSP-2018-11-011.pdf
   Google Scholar
• Tian, D. P. (2013). A review on image feature extraction and representation techniques. International Journal Multimedia Ubiquitous Engineering, 8(4), 385–395. https://gvpress.com/journals/IJMUE/vol8_no4/39.pdf
   Google Scholar
• Tsai, C.-F. (2012). Bag-of-words representation in image annotation: A review. ISRN Artificial Intelligence, 2012, 1–19. https://doi.org/10.5402/2012/376804
   Google Scholar
• Tyagi, V. (2017). Similarity measures and performance evaluation. In Content-based image retrieval (pp. 63–83). Springer Singapore.
   Google Scholar
• Tzelepi, M., & Tefas, A. (2018, January). Deep convolutional learning for content based image retrieval. Neurocomputing, 275, 2467–2478. https://doi.org/10.1016/j.neucom.2017.11.022
   Web of Science ®Google Scholar
• Varma, M., & Zisserman, A. (2009). CUReT1. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(11), 2032–2047. https://doi.org/10.1109/TPAMI.2008.182
   PubMedGoogle Scholar
• Vedaldi, A., & Zisserman, A., “Sparse kernel approximations for efficient classification and detection,” in 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, 2012, pp. 2320–2327.
   Google Scholar
• Vimina, E. R., & Divya, M. O. (2020, September). Maximal multi-channel local binary pattern with colour information for CBIR. Multimedia Tools and Applications, 79(35–36), 25357–25377. https://doi.org/10.1007/s11042-020-09207-8
   Web of Science ®Google Scholar
• Voulodimos, A., Doulamis, N., Doulamis, A., & Protopapadakis, E. (2018). Deep learning for computer vision: A brief review. Computational Intelligence and Neuroscience, 2018, 1–13. DOI: 10.1155/2018/7068349
   Web of Science ®Google Scholar
• Wan, J., “Deep learning for content-based image retrieval,” In Proceedings of the ACM International Conference on Multimedia - MM ’14, New York, USA, 2014, pp. 157–166.
   Google Scholar
• Wang, J. Z., Li, J., & Wiederholdy, G. (2000). SIMPLIcity: Semantics-sensitive Integrated Matching for Picture LIbraries. Lecture Notes in Computer Science, 1929 (9), 360–371. (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). DOI: 10.1007/3-540-40053-2_32
   Google Scholar
• Wang, Z., Fan, B., & Wu, F., “Local intensity order pattern for feature description,” in 2011 International Conference on Computer Vision, 2011, no. May, pp. 603–610. New York, USA.
   Google Scholar
• Watanabe, T., Ito, S., & Yokoi, K. (2010). Co-occurrence histograms of oriented gradients for human detection. IPSJ Transactions on Computer Vision and Applications, 2, 39–47. https://doi.org/10.2197/ipsjtcva.2.39
   Google Scholar
• Won, C. S. W., Park, D. K. P., & Park, S.-J. P. (2002, February). Efficient use of MPEG-7 edge histogram descriptor. ETRI Journal, 24(1), 23–30. https://doi.org/10.4218/etrij.02.0102.0103
   Web of Science ®Google Scholar
• Wu, G.-D., & Huang, P.-H. (2013, February). A Vectorization-optimization-method-based type-2 fuzzy neural network for noisy data classification. IEEE Transactions on Fuzzy Systems, 21(1), 1–15. https://doi.org/10.1109/TFUZZ.2012.2197754
   Web of Science ®Google Scholar
• Xu, D., Cham, T. J., Yan, S., Duan, L., & Chang, S. (2010). Aligned pyramid matching. IEEE Transactions on Circuits and Systems, 20(8), 1068–1079. https://doi.org/10.1109/TCSVT.2010.2051286
   Google Scholar
• Xu, Y., Zhao, X., & Gong, J. (2019). A large-scale secure image retrieval method in cloud environment. IEEE Access, 7, 160082–160090. https://doi.org/10.1109/ACCESS.2019.2951175
   Web of Science ®Google Scholar
• Yang, H.-F., Lin, K., & Chen, C.-S. (2018, February). Supervised learning of semantics-preserving hash via deep convolutional neural networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(2), 437–451. https://doi.org/10.1109/TPAMI.2017.2666812
   PubMed Web of Science ®Google Scholar
• Yoon, H., Park, C.-S., Kim, J. S., & Baek, J.-G. (2013, January). Algorithm learning based neural network integrating feature selection and classification. Expert Systems with Applications, 40(1), 231–241. https://doi.org/10.1016/j.eswa.2012.07.018
   Web of Science ®Google Scholar
• Younus, Z. S., Mohamad, D., Saba, T., Alkawaz, H. M., Rehman, A., Al-Rodhaan, M., Al-Dhelaan, A. (2015, August). Content-based image retrieval using PSO and k-means clustering algorithm. Arabic Journal Geoscience, 8(8), 6211–6224. doi: 10.1007/s12517-014-1584-7
   Google Scholar
• Yousuf, M., Mehmood, Z., Habib, H. A., Mahmood, T., Saba, T., Rehman, A., & Rashid, M. (2018). A novel technique based on visual words fusion analysis of sparse features for effective content-based image retrieval. Mathematical Problems in Engineering, 2018, 1–13. https://doi.org/10.1155/2018/2134395
   Web of Science ®Google Scholar
• Zhang, D., Islam, M. M., & Lu, G. (2012, January). A review on automatic image annotation techniques. Pattern Recognition, 45(1), 346–362. https://doi.org/10.1016/j.patcog.2011.05.013
   Web of Science ®Google Scholar
• Zhang, D., & Lu, G., “A comparative study on shape retrieval using Fourier descriptiors with different shape signatures,” in Intelligent Multimedia, Computing and Communications: Technologies and Applications of the Future: Proceedings of the International Conference on Intellient Multimedia and Distance Education, 2001, pp. 1–9.
   Google Scholar
• Zhang, D., & Lu, G. (2004, January). Review of shape representation and description techniques. Pattern Recognition, 37(1), 1–19. https://doi.org/10.1016/j.patcog.2003.07.008
   Web of Science ®Google Scholar
• Zhang, J., Marszałek, M., Lazebnik, S., & Schmid, C. (2007). Local features and kernels for classification of texture and object categories: A comprehensive study. International Journal of Computational Vision and Robotics, 73(2), 213–238. https://doi.org/10.1007/s11263-006-9794-4
   Google Scholar
• Zhao, M., Zhang, H., & Sun, J. (2016, July). A novel image retrieval method based on multi-trend structure descriptor. Journal of Visual Communication and Image Representation, 38, 73–81. https://doi.org/10.1016/j.jvcir.2016.02.016
   Web of Science ®Google Scholar
• Zhao, Z., Tian, Q., Sun, H., Jin, X., & Guo, J. (2016, January). Content based image retrieval scheme using color, texture and shape features. International Journal of Signal Processing Image Processing Pattern Recognition, 9(1), 203–212. doi: 10.14257/ijsip.2016.9.1.19
   Google Scholar
• Zheng, Q., Tian, X., Yang, M., & Wang, H. (2019). Differential learning: A powerful tool for interactive content-based image retrieval. Engineering Letters, 27(1), 202–215. http://www.engineeringletters.com/issues_v27/issue_1/EL_27_1_23.pdf
   Web of Science ®Google Scholar
• Zheng, X., Tang, B., Gao, Z., Liu, E., & Luo, W. (2016, November). Study on image retrieval based on image texture and color statistical projection. Neurocomputing, 215, 217–224. https://doi.org/10.1016/j.neucom.2015.07.157
   Web of Science ®Google Scholar
• Zhuo, L., Cheng, B., & Zhang, J. (2014). A comparative study of dimensionality reduction methods for large-scale image retrieval. Neurocomputing, 141, 202–210. https://doi.org/10.1016/j.neucom.2014.03.014
   Web of Science ®Google Scholar