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Iranian Architectural Styles Recognition Using Image Processing and Deep Learning

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Dynamics of Information Systems (DIS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14321))

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Abstract

Iranian architecture, also known as Persian architecture, encompasses the design of buildings in Iran and extends to various regions in West Asia, the Caucasus, and Central Asia. With a rich history dating back at least 5,000 BC, it boasts distinctive features and styles. Iran, located in the Middle East, has faced ongoing geopolitical challenges, including the potential for conflicts, such as those in Iraq and Afghanistan. Unfortunately, historical monuments often become unintentional casualties during wartime, suffering damage or destruction. These historical monuments hold cultural and historical significance not only for the country they belong to but for all of humanity. Therefore, it is crucial to make efforts to preserve them. In this paper, we propose the development of an automated system utilizing Deep Learning methods for the detection and recognition of historical monuments. This system can be integrated into military equipment to help identify the architectural style of a building and determine its construction date. By doing so, it can provide a critical warning to prevent the targeting of historically significant structures. To support our system, we have curated a dataset consisting of approximately 3,000 photographs showcasing six distinct styles of Iranian historical architecture. Figure 1 provides some examples of these photographs. It is worth noting that this dataset can be valuable for various scientific research projects and applications beyond our proposed system. Additionally, it offers tourists the opportunity to learn about Iranian historical monuments independently, using their mobile phones to access information about a monument’s historical period and architectural style, eliminating the need for a traditional guide. This initiative aims to safeguard the invaluable cultural heritage of Iran and neighboring regions, contributing to the collective preservation of these historical treasures.

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References

  1. Hutt, A.: Introducing Persian Architecture. By Arthur Upham Pope. (Library of Introductions to Persian Art.), p. 115. Oxford University Press, London (1969). £ 1.25. J. Roy. Asiatic Soc. 105(2), 170-1 (1973)

    Google Scholar 

  2. Bier, C.: The decagonal tomb tower at Maragha and its architectural context: lines of mathematical thought. Nexus Netw. J. 14(2), 251–273 (2012). https://doi.org/10.1007/s00004-012-0108-6

    Article  Google Scholar 

  3. Ardalan, N., Bakhtiar, L.: The Sense of Unity: The Sufi Tradition in Persian Architecture. University of Chicago Press (1973)

    Google Scholar 

  4. Helaine, S.: Contested cultural heritage: a selective historiography. In: Silverman, H. (ed.) Contested Cultural Heritage, pp. 1–49. Springer, New York (2010). https://doi.org/10.1007/978-1-4419-7305-4_1

    Chapter  Google Scholar 

  5. Grigor, T.: “They have not changed in 2,500 years”: art, archaeology, and modernity in Iran. In: Unmasking Ideology in Imperial and Colonial Archaeology: Vocabulary, Symbols, and Legacy. Ideas, Debates, and Perspectives, pp. 121–146 (2018)

    Google Scholar 

  6. Kou, F., Du, J., He, Y., Ye, L.: Social network search based on semantic analysis and learning. CAAI Trans. Intell. Technol. 1(4), 293–302 (2016)

    Article  Google Scholar 

  7. Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., Garcia-Rodriguez, J.: A review on deep learning techniques applied to semantic segmentation. arXiv preprint arXiv:170406857 (2017)

  8. Li, L.-J., Hao, Su., Lim, Y., Fei-Fei, Li.: Objects as attributes for scene classification. In: Kutulakos, K.N. (ed.) ECCV 2010. LNCS, vol. 6553, pp. 57–69. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-35749-7_5

    Chapter  Google Scholar 

  9. Srinivas, S., Sarvadevabhatla, R.K., Mopuri, K.R., Prabhu, N., Kruthiventi, S.S., Babu, R.V.: A taxonomy of deep convolutional neural nets for computer vision. Front. Robot. AI 2, 36 (2016)

    Article  Google Scholar 

  10. Sadeghi, Z., Alizadehsani, R., Cifci, M.A., Kausar, S., Rehman, R., Mahanta, P., et al.: A brief review of explainable artificial intelligence in healthcare. arXiv preprint arXiv:230401543 (2023)

  11. Khozeimeh, F., Alizadehsani, R., Shirani, M., Tartibi, M., Shoeibi, A., Alinejad-Rokny, H., et al.: ALEC: active learning with ensemble of classifiers for clinical diagnosis of coronary artery disease. Comput. Biol. Med. 158, 106841 (2023)

    Article  Google Scholar 

  12. Nasab, R.Z., Ghamsari, M.R.E., Argha, A., Macphillamy, C., Beheshti, A., Alizadehsani, R., et al.: Deep learning in spatially resolved transcriptomics: a comprehensive technical view. arXiv preprint arXiv:221004453 (2022)

  13. Asgharnezhad, H., Shamsi, A., Alizadehsani, R., Khosravi, A., Nahavandi, S., Sani, Z.A., et al.: Objective evaluation of deep uncertainty predictions for COVID-19 detection. Sci. Rep. 12(1), 1–11 (2022)

    Article  Google Scholar 

  14. Llamas, J., Lerones, P.M., Medina, R., Zalama, E., Gómez-García-Bermejo, J.: Classification of architectural heritage images using deep learning techniques. Appl. Sci. 7(10), 992 (2017)

    Google Scholar 

  15. Perez, H., Tah, J.H., Mosavi, A.: Deep learning for detecting building defects using convolutional neural networks. Sensors 19(16), 3556 (2019)

    Article  Google Scholar 

  16. Makantasis, K., Doulamis, N.D., Voulodimos, A.: Recognizing buildings through deep learning: a case study on half-timbered framed buildings in Calw city. In: VISIGRAPP (5: VISAPP) (2017)

    Google Scholar 

  17. Obeso, A.M., Benois-Pineau, J., Acosta, A.Á.R., Vázquez, M.S.G.: Architectural style classification of Mexican historical buildings using deep convolutional neural networks and sparse features. J. Electron. Imaging 26(1), 011016 (2017)

    Article  Google Scholar 

  18. Vailaya, A., Figueiredo, M.A., Jain, A.K., Zhang, H.-J.: Image classification for content-based indexing. IEEE Trans. Image Process. 10(1), 117–130 (2001)

    Article  Google Scholar 

  19. Schreiber, S., Agne, S., Wolf, I., Dengel, A., Ahmed, S.: DeepDeSRT: deep learning for detection and structure recognition of tables in document images. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). IEEE (2017)

    Google Scholar 

  20. Zhang, G., Pan, Y., Zhang, L.: Deep learning for detecting building façade elements from images considering prior knowledge. Autom. Constr. 133, 104016 (2022)

    Article  Google Scholar 

  21. Nahavandi, D., Alizadehsani, R., Khosravi, A., Acharya, U.R.: Application of artificial intelligence in wearable devices: opportunities and challenges. Comput. Methods Programs Biomed. 213, 106541 (2022)

    Article  Google Scholar 

  22. Alizadehsani, R., Sharifrazi, D., Izadi, N.H., Joloudari, J.H., Shoeibi, A., Gorriz, J.M., et al.: Uncertainty-aware semi-supervised method using large unlabeled and limited labeled COVID-19 data. ACM Trans. Multimedia Comput. Commun. Appl. (TOMM) 17(3s), 1–24 (2021)

    Google Scholar 

  23. Alizadehsani, R., Roshanzamir, M., Izadi, N.H., Gravina, R., Kabir, H.D., Nahavandi, D., et al.: Swarm intelligence in internet of medical things: a review. Sensors 23(3), 1466 (2023)

    Article  Google Scholar 

  24. Alizadehsani, R., Habibi, J., Bahadorian, B., Mashayekhi, H., Ghandeharioun, A., Boghrati, R., et al.: Diagnosis of coronary arteries stenosis using data mining. J. Med. Sig. Sens. 2(3), 153 (2012)

    Article  Google Scholar 

  25. Zangooei, M.H., Habibi, J., Alizadehsani, R.: Disease Diagnosis with a hybrid method SVR using NSGA-II. Neurocomputing 136, 14–29 (2014)

    Article  Google Scholar 

  26. Alizadehsani, R., Hosseini, M.J., Boghrati, R., Ghandeharioun, A., Khozeimeh, F., Sani, Z.A.: Exerting cost-sensitive and feature creation algorithms for coronary artery disease diagnosis. Int. J. Knowl. Discov. Bioinform. (IJKDB). 3(1), 59–79 (2012)

    Article  Google Scholar 

  27. Alizadehsani, R., Habibi, J., Sani, Z.A., Mashayekhi, H., Boghrati, R., Ghandeharioun, A., et al.: Diagnosis of coronary artery disease using data mining based on lab data and echo features. J. Med. Bioeng. 1(1), 26–29 (2012)

    Google Scholar 

  28. Nematollahi, M.A., Askarinejad, A., Asadollahi, A., Salimi, M., Moghadami, M., Sasannia, S., et al.: Association and predictive capability of body composition and diabetes mellitus using artificial intelligence: a cohort study (2022)

    Google Scholar 

  29. Sharifrazi, D., Alizadehsani, R., Hoseini Izadi, N., Roshanzamir, M., Shoeibi, A., Khozeimeh, F., et al.: Hypertrophic cardiomyopathy diagnosis based on cardiovascular magnetic resonance using deep learning techniques. Colour Filtering (2021)

    Google Scholar 

  30. Shoeibi, A., Khodatars, M., Jafari, M., Ghassemi, N., Moridian, P., Alizadesani, R., et al.: Diagnosis of brain diseases in fusion of neuroimaging modalities using deep learning: a review. Inf. Fusion 93, 85–117 (2022)

    Article  Google Scholar 

  31. Sharifrazi, D., Alizadehsani, R., Joloudari, J.H., Shamshirband, S., Hussain, S., Sani, Z.A., et al.: CNN-KCL: automatic myocarditis diagnosis using convolutional neural network combined with k-means clustering (2020)

    Google Scholar 

  32. Joloudari, J.H., Hussain, S., Nematollahi, M.A., Bagheri, R., Fazl, F., Alizadehsani, R., et al.: BERT-deep CNN: state-of-the-art for sentiment analysis of COVID-19 tweets. arXiv preprint arXiv:221109733 (2022)

  33. Moridian, P., Shoeibi, A., Khodatars, M., Jafari, M., Pachori, R.B., Khadem, A., et al.: Automatic diagnosis of sleep apnea from biomedical signals using artificial intelligence techniques: methods, challenges, and future works. Wiley Interdisc. Rev. Data Min. Knowl. Discov. 12(6), e1478 (2022)

    Article  Google Scholar 

  34. Alizadehsani, R., Roshanzamir, M., Abdar, M., Beykikhoshk, A., Zangooei, M.H., Khosravi, A., et al.: Model uncertainty quantification for diagnosis of each main coronary artery stenosis. Soft. Comput. 24, 10149–10160 (2020). https://doi.org/10.1007/s00500-019-04531-0

    Article  Google Scholar 

  35. Nahavandi, S., Alizadehsani, R., Nahavandi, D., Mohamed, S., Mohajer, N., Rokonuzzaman, M., et al.: A comprehensive review on autonomous navigation. arXiv preprint arXiv:221212808 (2022)

  36. Khalili, H., Rismani, M., Nematollahi, M.A., Masoudi, M.S., Asadollahi, A., Taheri, R., et al.: Prognosis prediction in traumatic brain injury patients using machine learning algorithms. Sci. Rep. 13(1), 960 (2023)

    Article  Google Scholar 

  37. Abedini, S.S., Akhavan, S., Heng, J., Alizadehsani, R., Dehzangi, I., Bauer, D.C., et al.: A critical review of the impact of candidate copy number variants on autism spectrum disorders. arXiv preprint arXiv:230203211 (2023)

  38. Karami, M., Alizadehsani, R., Argha, A., Dehzangi, I., Alinejad-Rokny, H.: Revolutionizing genomics with reinforcement learning techniques. arXiv preprint arXiv:230213268 (2023)

  39. Jafari, M., Shoeibi, A., Khodatars, M., Ghassemi, N., Moridian, P., Delfan, N., et al.: Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: a review. arXiv preprint arXiv:221014909 (2022)

  40. Roshanzamir, M., Alizadehsani, R., Roshanzamir, M., Shoeibi, A., Gorriz, J.M., Khosrave, A., et al.: What happens in face during a facial expression? Using data mining techniques to analyze facial expression motion vectors. arXiv preprint arXiv:210905457 (2021)

  41. Ayoobi, N., Sharifrazi, D., Alizadehsani, R., Shoeibi, A., Gorriz, J.M., Moosaei, H., et al.: Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods. Results Phys. 27, 104495 (2021)

    Article  Google Scholar 

  42. Sharifrazi, D., Alizadehsani, R., Roshanzamir, M., Joloudari, J.H., Shoeibi, A., Jafari, M., et al.: Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images. Biomed. Sig. Process. Control 68, 102622 (2021)

    Article  Google Scholar 

  43. Alizadehsani, R., Roshanzamir, M., Hussain, S., Khosravi, A., Koohestani, A., Zangooei, M.H., et al.: Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020). Ann. Oper. Res. 1–42 (2021). https://doi.org/10.1007/s10479-021-04006-2

  44. Bidhend, M.Q., Abdollahzadeh, M.M.: Architecture, Land, and Man Rereading, reviewing and criticism of Pirnia’s suggested principles for Iranian architecture. Art Alchemy/Kimiaye-Honar 3(12), 72–99 (2014)

    Google Scholar 

  45. Hassannataj Joloudari, J., Azizi, F., Nematollahi, M.A., Alizadehsani, R., Hassannatajjeloudari, E., Nodehi, I., et al.: GSVMA: a genetic support vector machine ANOVA method for CAD diagnosis. Front. Cardiovasc. Med. 8, 2178 (2022)

    Article  Google Scholar 

  46. Iqbal, M.S., Ahmad, W., Alizadehsani, R., Hussain, S., Rehman, R.: Breast cancer dataset, classification and detection using deep learning. In: Healthcare. MDPI (2022)

    Google Scholar 

  47. Alizadehsani, R., Roshanzamir, M., Abdar, M., Beykikhoshk, A., Khosravi, A., Nahavandi, S., et al.: Hybrid genetic-discretized algorithm to handle data uncertainty in diagnosing stenosis of coronary arteries. Expert. Syst. 39(7), e12573 (2022)

    Article  Google Scholar 

  48. Joloudari, J.H., Alizadehsani, R., Nodehi, I., Mojrian, S., Fazl, F., Shirkharkolaie, S.K., et al.: Resource allocation optimization using artificial intelligence methods in various computing paradigms: a review. arXiv preprint arXiv:220312315 (2022)

  49. Kakhi, K., Alizadehsani, R., Kabir, H.D., Khosravi, A., Nahavandi, S., Acharya, U.R.: The internet of medical things and artificial intelligence: trends, challenges, and opportunities. Biocybern. Biomed. Eng. 42(3), 749–771 (2022)

    Article  Google Scholar 

  50. Alizadehsani, R., Eskandarian, R., Behjati, M., Zahmatkesh, M., Roshanzamir, M., Izadi, N.H., et al.: Factors associated with mortality in hospitalized cardiovascular disease patients infected with COVID-19. Immun. Inflamm. Dis. 10(3), e561 (2022)

    Article  Google Scholar 

  51. Mahami, H., Ghassemi, N., Darbandy, M.T., Shoeibi, A., Hussain, S., Nasirzadeh, F., et al.: Material recognition for automated progress monitoring using deep learning methods. arXiv preprint arXiv:200616344 (2020)

  52. Shoushtarian, M., Alizadehsani, R., Khosravi, A., Acevedo, N., McKay, C.M., Nahavandi, S., et al.: Objective measurement of tinnitus using functional near-infrared spectroscopy and machine learning. PLoS ONE 15(11), e0241695 (2020)

    Article  Google Scholar 

  53. Javan, A.A.K., Jafari, M., Shoeibi, A., Zare, A., Khodatars, M., Ghassemi, N., et al.: Medical images encryption based on adaptive-robust multi-mode synchronization of chen hyper-chaotic systems. Sensors 21(11), 3925 (2021)

    Article  Google Scholar 

  54. Eskandarian, R., Alizadehsani, R., Behjati, M., Zahmatkesh, M., Sani, Z.A., Haddadi, A., et al.: Identification of clinical features associated with mortality in COVID-19 patients. Oper. Res. Forum (2023). https://doi.org/10.1007/s43069-022-00191-3

  55. Joloudari, J.H., Mojrian, S., Nodehi, I., Mashmool, A., Zadegan, Z.K., Shirkharkolaie, S.K., et al.: Application of artificial intelligence techniques for automated detection of myocardial infarction: a review. Physiol. Measur. 1–16 (2022)

    Google Scholar 

  56. Islam, S.M.S., Talukder, A., Awal, M., Siddiqui, M., Umer, M., Ahamad, M., et al.: Machine learning approaches for predicting hypertension and its associated factors using population-level data from three south asian countries. Front. Cardiovasc. Med. 9, 762 (2022)

    Article  Google Scholar 

  57. Joloudari, J.H., Azizi, F., Nodehi, I., Nematollahi, M.A., Kamrannejhad, F., Mosavi, A., et al.: DNN-GFE: a deep neural network model combined with global feature extractor for COVID-19 diagnosis based on CT scan images. Easychair, Manchester (2021)

    Google Scholar 

  58. Khozeimeh, F., Sharifrazi, D., Izadi, N.H., Joloudari, J.H., Shoeibi, A., Alizadehsani, R., et al.: RF-CNN-F: random forest with convolutional neural network features for coronary artery disease diagnosis based on cardiac magnetic resonance. Sci. Rep. 12(1), 11178 (2022)

    Article  Google Scholar 

  59. Jafari, M., Shoeibi, A., Ghassemi, N., Heras, J., Khosravi, A., Ling, S.H., et al.: Automatic diagnosis of myocarditis disease in cardiac MRI modality using deep transformers and explainable artificial intelligence. arXiv preprint arXiv:221014611 (2022)

  60. Moravvej, S.V., Alizadehsani, R., Khanam, S., Sobhaninia, Z., Shoeibi, A., Khozeimeh, F., et al.: RLMD-PA: a reinforcement learning-based myocarditis diagnosis combined with a population-based algorithm for pretraining weights. Contrast Media Mol. Imaging 2022, 1–15 (2022)

    Google Scholar 

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Authors and Affiliations

  1. School of Architecture, Islamic Azad University Taft, 8991985495, Taft, Iran

    Mohammad Tayarani Darbandy

  2. Sadjad University, Mashhad, Iran

    Benyamin Zojaji

  3. Mashhad University of Medical Science, Mashhad, Iran

    Fariba Alizadeh Sani

Authors
  1. Mohammad Tayarani Darbandy
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  2. Benyamin Zojaji
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  3. Fariba Alizadeh Sani
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Correspondence to Mohammad Tayarani Darbandy .

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Editors and Affiliations

  1. Jan Evangelista Purkyně University, Ústí nad Labem-město, Czech Republic

    Hossein Moosaei

  2. Charles University, Prague, Czech Republic

    Milan Hladík

  3. University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

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Darbandy, M.T., Zojaji, B., Sani, F.A. (2024). Iranian Architectural Styles Recognition Using Image Processing and Deep Learning. In: Moosaei, H., Hladík, M., Pardalos, P.M. (eds) Dynamics of Information Systems. DIS 2023. Lecture Notes in Computer Science, vol 14321. Springer, Cham. https://doi.org/10.1007/978-3-031-50320-7_5

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  • DOI: https://doi.org/10.1007/978-3-031-50320-7_5

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