An improved similarity matching model for the content-based image retrieval model

[1] D. Srivastava, S. S. Singh, B. Rajitha, M. Verma, M. Kaur and H. -N. Lee, “Content-Based Image Retrieval: A Survey on Local and Global Features Selection, Extraction, Representation, and Evaluation Parameters,” in IEEE Access, vol. 11, pp. 95410–95431, 2023, doi: 10.1109/ACCESS.2023.3308911. SrivastavaD. SinghS. S. RajithaB. VermaM. KaurM. LeeH. -N. “Content-Based Image Retrieval: A Survey on Local and Global Features Selection, Extraction, Representation, and Evaluation Parameters,” IEEE Access 11 95410 95431 2023 10.1109/ACCESS.2023.3308911 Open DOISearch in Google Scholar

[2] G. Sumbul, J. Xiang and B. Demir, “Towards Simultaneous Image Compression and Indexing for Scalable Content-Based Retrieval in Remote Sensing,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–12, 2022, Art no. 5630912, doi: 10.1109/TGRS.2022.3204914. SumbulG. XiangJ. DemirB. “Towards Simultaneous Image Compression and Indexing for Scalable Content-Based Retrieval in Remote Sensing,” in IEEE Transactions on Geoscience and Remote Sensing 60 1 12 2022 Art no. 5630912, 10.1109/TGRS.2022.3204914 Open DOISearch in Google Scholar

[3] Cengiz Pehlevan, Anirvan M Sengupta, and Dmitri B Chklovskii. Why do similarity matching objectives lead to hebbian/anti-hebbian networks? Neural computation, 30(1):84–124, 2018. CengizPehlevan SenguptaAnirvan M ChklovskiiDmitri B Why do similarity matching objectives lead to hebbian/anti-hebbian networks? Neural computation 30 1 84 124 2018 Search in Google Scholar

[4] Liu, G.-H.; Yang, J.-Y. Content-based image retrieval using color difference histogram. Pattern Recognit. 2013, 46, 188–198. LiuG.-H. YangJ.-Y. Content-based image retrieval using color difference histogram Pattern Recognit. 2013 46 188 198 Search in Google Scholar

[5] Tian, D. Support Vector Machine for Content-based Image Retrieval: A Comprehensive Overview. J. Inf. Hiding Multim. Signal Process. 2018, 9, 1464–1478. TianD. Support Vector Machine for Content-based Image Retrieval: A Comprehensive Overview J. Inf. Hiding Multim. Signal Process. 2018 9 1464 1478 Search in Google Scholar

[6] Mehmood, Z.; Mahmood, T.; Javid, M.A. Content-based image retrieval and semantic automatic image annotation based on the weighted average of triangular histograms using support vector machine. Appl. Intell. 2018, 48, 166–181. MehmoodZ. MahmoodT. JavidM.A. Content-based image retrieval and semantic automatic image annotation based on the weighted average of triangular histograms using support vector machine Appl. Intell. 2018 48 166 181 Search in Google Scholar

[7] Bay, Herbert, Tinne Tuytelaars, and Luc Van Gool. “SURF: Speeded Up Robust Features.” Computer Vision – ECCV 2006 Lecture Notes in Computer Science (2006): 404–17. Web. BayHerbert TuytelaarsTinne Van GoolLuc “SURF: Speeded Up Robust Features.” Computer Vision – ECCV 2006 Lecture Notes in Computer Science 2006 404 17 Web. Search in Google Scholar

[8] Lindeberg, T.: Feature detection with automatic scale selection. IJCV 30(2) (1998) 79–116. LindebergT. Feature detection with automatic scale selection IJCV 30 2 1998 79 116 Search in Google Scholar

[9] Mikolajczyk, K., Schmid, C.: Indexing based on scale invariant interest points. In: ICCV. Volume 1. (2001) 525–531. MikolajczykK. SchmidC. Indexing based on scale invariant interest points In: ICCV 1 2001 525 531 Search in Google Scholar

[10] H. Tamura, S. Mori, and Y. Yamawaki, “Textural Features Corresponding to Visual Perception”, IEEE Transactions on Systems, Man, and Cybernetics, SMC-8, (1978), pp. 460–473. TamuraH. MoriS. YamawakiY. “Textural Features Corresponding to Visual Perception” IEEE Transactions on Systems, Man, and Cybernetics, SMC-8 1978 460 473 Search in Google Scholar

[11] P. Manipoonchelvi and K. Muneeswaran, Multi region-based image retrieval system, Sadhana Indian Acad. Sci. 39 (2014), 333–344. ManipoonchelviP. MuneeswaranK. Multi region-based image retrieval system Sadhana Indian Acad. Sci. 39 2014 333 344 Search in Google Scholar

[12] Radenović F., Iscen A., Tolias G., Avrithis Y., Chum O. Revisiting Oxford, and Paris: Large-Scale Image Retrieval Benchmarking, CVPR, 2018. RadenovićF. IscenA. ToliasG. AvrithisY. ChumO Revisiting Oxford, and Paris Large-Scale Image Retrieval Benchmarking, CVPR 2018 Search in Google Scholar

[13] Ashraf, R.; Bashir, K.; Irtaza, A.; Mahmood, M.T. Content Based Image Retrieval Using Embedded Neural Networks with Bandletized Regions. Entropy 2015, 17, 3552–3580. AshrafR. BashirK. IrtazaA. MahmoodM.T. Content Based Image Retrieval Using Embedded Neural Networks with Bandletized Regions Entropy 2015 17 3552 3580 Search in Google Scholar

[14] Lin Feng, Jun Wu, Shenglan Liu, Hongwei Zhang, Global Correlation Descriptor: A novel image representation for image retrieval, Journal of Visual Communication, and Image Representation, Volume 33, 2015, Pages 104–114. FengLin WuJun LiuShenglan ZhangHongwei Global Correlation Descriptor: A novel image representation for image retrieval Journal of Visual Communication, and Image Representation 33 2015 104 114 Search in Google Scholar

[15] Seetharaman, K. and Selvaraj, S., 2016. Statistical tests of hypothesis based color image retrieval. Journal of Data Analysis and Information Processing, 4(2), pp.90–99. SeetharamanK. SelvarajS. 2016 Statistical tests of hypothesis based color image retrieval Journal of Data Analysis and Information Processing 4 2 90 99 Search in Google Scholar

[16] Abhishek Jain, Aman Jain, Nihal Chauhan, Vikrant Singh, Narina Thakur, “Information Retrieval using Cosine and Jaccard Similarity Measures in Vector Space Model”, International Journal of Computer Applications, Volume 164, No 6, PP.28–30, 2017. JainAbhishek JainAman ChauhanNihal SinghVikrant ThakurNarina “Information Retrieval using Cosine and Jaccard Similarity Measures in Vector Space Model” International Journal of Computer Applications 164 6 28 30 2017 Search in Google Scholar

[17] Komal Maher, Madhuri S. Joshi, “Effectiveness of Different Similarity Measures for Text Classification and Clustering”, International Journal of Computer Science and Information Technologies, Vol. 7, No.4, pp. 1715–1720, 2016. MaherKomal JoshiMadhuri S. “Effectiveness of Different Similarity Measures for Text Classification and Clustering” International Journal of Computer Science and Information Technologies 7 4 1715 1720 2016 Search in Google Scholar

[18] Obeid, D., Ramambason, H., and Pehlevan, C. (2019). Structured and deep similarity matching via structured and deep hebbian networks. In Advances in Neural Information Processing Systems, pages 15377–15386. ObeidD. RamambasonH. PehlevanC. 2019 Structured and deep similarity matching via structured and deep hebbian networks In Advances in Neural Information Processing Systems 15377 15386 Search in Google Scholar

[19] D.M Raj, R. Mohanasundaram (2020), “A New Improved Filter-based Feature Selection Model for High Dimensional Data” Journal of Supercomputing, Springer, volume No. 76, Issue No. 3, pp 5745–5762. RajD.M MohanasundaramR. 2020 “A New Improved Filter-based Feature Selection Model for High Dimensional Data” Journal of Supercomputing, Springer 76 3 5745 5762 Search in Google Scholar

[20] O. Siméoni, Y. Avrithis and O. Chum, “Local Features and Visual Words Emerge in Activations,” 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019, pp. 11643–11652, doi: 10.1109/CVPR.2019.01192. SiméoniO. AvrithisY. ChumO. “Local Features and Visual Words Emerge in Activations,” 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Long Beach, CA, USA 2019 11643 11652 10.1109/CVPR.2019.01192 Open DOISearch in Google Scholar

[21] Moheb Ramzy Girgis, Abdelmgeid Amin Aly & Fatima Mohy Eldin Azzam “The Effect of Similarity Measures on Genetic Algorithm-Based Information Retrieval”, International Journal of Computer Science Engineering and Information Technology Research. Vol. 4, Issue 5, Oct 2014, pp. 91–100. GirgisMoheb Ramzy AlyAbdelmgeid Amin Eldin AzzamFatima Mohy “The Effect of Similarity Measures on Genetic Algorithm-Based Information Retrieval” International Journal of Computer Science Engineering and Information Technology Research 4 5 Oct 2014 91 100 Search in Google Scholar

[22] A. El-Nouby, N. Neverova, I. Laptev, and H. Jegou, “Training vision transformers for image retrieval,” arXiv preprint arXiv:2102.05644, 2021, doi. org/10.48550/arXiv.2102.05644. El-NoubyA. NeverovaN. LaptevI. JegouH. “Training vision transformers for image retrieval,” arXiv preprint arXiv:2102.05644, 2021 doi. org/10.48550/arXiv.2102.05644. Search in Google Scholar

[23] Y. Zhang, Q. Qian, H. Wang, C. Liu, W. Chen and F. Wang, “Graph Convolution Based Efficient Re-Ranking for Visual Retrieval,” in IEEE Transactions on Multimedia, doi: 10.1109/TMM.2023.3276167. ZhangY. QianQ. WangH. LiuC. ChenW. WangF. “Graph Convolution Based Efficient Re-Ranking for Visual Retrieval,” in IEEE Transactions on Multimedia 10.1109/TMM.2023.3276167 Open DOISearch in Google Scholar

[24] X. Zhu, H. Wang, P. Liu, Z. Yang, and J. Qian, “Graph-based reasoning attention pooling with curriculum design for content-based image retrieval,” Image and Vision Computing, vol. 115, p. 104289, 2021, doi.org/10.1016/j.imavis.2021.104289. ZhuX. WangH. LiuP. YangZ. QianJ. “Graph-based reasoning attention pooling with curriculum design for content-based image retrieval,” Image and Vision Computing 115 104289 2021 doi.org/10.1016/j.imavis.2021.104289. Search in Google Scholar

[25] Raj, R. Mohanasundaram (2020), “An Efficient Filter-Based Feature Selection Model to Identify Significant Features from High-Dimensional Microarray Data” Arabian Journal for Science and Engineering”, Springer, volume-45, pp. 2619–2630. RajR. Mohanasundaram 2020 “An Efficient Filter-Based Feature Selection Model to Identify Significant Features from High-Dimensional Microarray Data” Arabian Journal for Science and Engineering” Springer, volume-45 2619 2630 Search in Google Scholar

[26] T. Sutojo, P. S. Tirajani, D. R. Ignatius Moses Setiadi, C. A. Sari and E. H. Rachmawanto, “CBR for classification of cow types using GLCM and color features extraction,” 2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE), Yogyakarta, Indonesia, 2017, pp. 182–187, doi: 10.1109/ICITISEE.2017.8285491. SutojoT. TirajaniP. S. Ignatius Moses SetiadiD. R. SariC. A. RachmawantoE. H. “CBR for classification of cow types using GLCM and color features extraction,” 2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE) Yogyakarta, Indonesia 2017 182 187 10.1109/ICITISEE.2017.8285491 Open DOISearch in Google Scholar

[27] Chauhan, S., Prasad, R., Saurabh, P., Mewada, P. (2018). Dominant and LBP-Based Content Image Retrieval Using Combination of Color, Shape and Texture Features. In: Pattnaik, P., Rautaray, S., Das, H., Nayak, J. (eds) Progress in Computing, Analytics and Networking. Advances in Intelligent Systems and Computing, vol 710. Springer, Singapore. DOI: 10.1007/978-981-10-7871-2_23. ChauhanS. PrasadR. SaurabhP. MewadaP. 2018 Dominant and LBP-Based Content Image Retrieval Using Combination of Color, Shape and Texture Features In: PattnaikP. RautarayS. DasH. NayakJ. (eds) Progress in Computing, Analytics and Networking. Advances in Intelligent Systems and Computing 710 Springer Singapore 10.1007/978-981-10-7871-2_23 Open DOISearch in Google Scholar

[28] Sengupta, A., Pehlevan, C., Tepper, M., Genkin, A., and Chklovskii, D. (2018). Manifold-tiling localized receptive _elds are optimal in similarity-preserving neural networks. In Advances in Neural Information Processing Systems, pp.7080–7090. SenguptaA. PehlevanC. TepperM. GenkinA. ChklovskiiD. 2018 Manifold-tiling localized receptive _elds are optimal in similarity-preserving neural networks In Advances in Neural Information Processing Systems 7080 7090 Search in Google Scholar

[29] Manimegalai A, Dr. Josephine Prem Kumar, “Automating Image Retrieval using UIpath (RPA) by Extricating Color Feature with String comparison in CBIR”, International Journal of New Innovations in Engineering and Technology (IJNIET), Volume14 Issue 2-July2020, pp.14–20. ManimegalaiA KumarJosephine PremDr. “Automating Image Retrieval using UIpath (RPA) by Extricating Color Feature with String comparison in CBIR” International Journal of New Innovations in Engineering and Technology (IJNIET) Volume14 Issue 2-July2020, 14 20 Search in Google Scholar

[30] Manimegalai A, Sonika, Sunil KK, Sunil BA, Vishwas V, “Enhancing Signature Forgery Detection System using CNN-SVM”, International Journal of Innovative Research in information Security, Volume 10, Issue 04, May 2024, DOI: 10.26562/ijiris.2024.v1004.33. ManimegalaiA Sonika SunilKK SunilBA VishwasV “Enhancing Signature Forgery Detection System using CNN-SVM” International Journal of Innovative Research in information Security 10 04 May 2024 10.26562/ijiris.2024.v1004.33 Open DOISearch in Google Scholar

[31] H. Wu, M. Wang, W. Zhou, H. Li, and Q. Tian, “Contextual similarity distillation for asymmetric image retrieval,” IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9489–9498, 2022, doi: 10.1109/cvpr52688.2022.00927. WuH. WangM. ZhouW. LiH. TianQ. “Contextual similarity distillation for asymmetric image retrieval,” IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 9489 9498 2022 10.1109/cvpr52688.2022.00927 Open DOISearch in Google Scholar

[32] Y. Song, R. Zhu, M. Yang, and D. He, “Dalg: Deep attentive local and global modeling for image retrieval,” arXiv preprint arXiv:2207.00287, 2022. SongY. ZhuR. YangM. HeD. “Dalg: Deep attentive local and global modeling for image retrieval,” arXiv preprint arXiv:2207.00287, 2022 Search in Google Scholar

[33] H. Wu, M. Wang, W. Zhou, H. Li, and Q. Tian, “Contextual similarity distillation for asymmetric image retrieval,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 9489–9498. WuH. WangM. ZhouW. LiH. TianQ. “Contextual similarity distillation for asymmetric image retrieval,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2022 9489 9498 Search in Google Scholar

[34] H. Gu, J. Li, G. Fu, C. Wong, X. Chen, and J. Zhu, “Autoloss GMS: Searching generalized margin-based softmax loss function for person re-identification,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 4744–4753. GuH. LiJ. FuG. WongC. ChenX. ZhuJ. “Autoloss GMS: Searching generalized margin-based softmax loss function for person re-identification,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2022 4744 4753 Search in Google Scholar

[35] G. Wu, X. Zhu, and S. Gong, “Learning hybrid ranking representation for person re-identification,” Pattern Recognition, vol. 121, p. 108239, 2022. WuG. ZhuX. GongS. “Learning hybrid ranking representation for person re-identification,” Pattern Recognition 121 108239 2022 Search in Google Scholar

[36] T. Si, F. He, H. Wu, and Y. Duan, “Spatial-driven features based on image dependencies for person re-identification,” Pattern Recognition, vol. 124, p. 108462, 2022. SiT. HeF. WuH. DuanY. “Spatial-driven features based on image dependencies for person re-identification,” Pattern Recognition 124 108462 2022 Search in Google Scholar

[37] K. Zhou, Y. Yang, A. Cavallaro, and T. Xiang, “Learning generalizable omni-scale representations for person re-identification,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 9, pp. 5056–5069, 2022. ZhouK. YangY. CavallaroA. XiangT. “Learning generalizable omni-scale representations for person re-identification,” IEEE Transactions on Pattern Analysis and Machine Intelligence 44 9 5056 5069 2022 Search in Google Scholar

[38] Panel Giriraj Gautam, Anita Khanna, “Content Based Image Retrieval System Using CNN based Deep Learning Models”, Elsevier, Vol 235, Pages 3131–3141, 2024, DOI: 10.1016/j.procs.2024.04.296 GautamPanel Giriraj KhannaAnita “Content Based Image Retrieval System Using CNN based Deep Learning Models” Elsevier, 235 3131 3141 2024 10.1016/j.procs.2024.04.296 Open DOISearch in Google Scholar

[39] Chi Zhang, Jie Liu, “Content Based Deep Learning Image Retrieval: A Survey”, ICCIP 2023: 2023 the 9th International Conference on Communication and Information Processing (ICCIP), DOI: 10.1145/3638884.3638908 ZhangChi LiuJie “Content Based Deep Learning Image Retrieval: A Survey” ICCIP 2023: 2023 the 9th International Conference on Communication and Information Processing (ICCIP) 10.1145/3638884.3638908 Open DOISearch in Google Scholar

[40] Yiwei Jia, Yiwei Jia, Shiyong Huang, Xueming Li, “HFFR-SR: Hierarchical Fusion Feature Representations for Super Resolution of Old Images”, ICCIP 2023: 2023 the 9th International Conference on Communication and Information Processing (ICCIP), pages 1–5, DOI: 10.1145/3638884.3638885. JiaYiwei JiaYiwei HuangShiyong LiXueming “HFFR-SR: Hierarchical Fusion Feature Representations for Super Resolution of Old Images” ICCIP 2023: 2023 the 9th International Conference on Communication and Information Processing (ICCIP) 1 5 10.1145/3638884.3638885 Open DOISearch in Google Scholar