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Improving recognition of deteriorated historical Persian geometric patterns by fusion decision methods

  • S.I.: Visual Pattern Recognition and Extraction for Cultural Heritage
  • Published:
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Abstract

Historical architecture has different special styles attributed to each era, dynasty, or region. These styles are common features such as geometric properties, ratios, scales, colors, and artistic techniques. Historical geometric ornaments have an enormous capability for classification based on their geometric characteristics. Smart pattern recognition allows researchers to classify huge databases of heritage for useful internet searches. So, our main goal in this paper is to implement the detection of categories in geometric patterns for classification and documentation in which by the photography of ornaments in every monument, the type of patterns and the number of every type of pattern would be estimated quickly. Furthermore, due to occurring deterioration in these patterns, our method also contributes to recognizing the deteriorated patterns. When we encounter numerous pieces of deteriorated patterns, manual recognition in order to reassemble and reconstruct is usually impossible or time-consuming. With the aid of artificial intelligence, in this paper, our aim is to seek to solve the automatic recognition of historical geometric patterns, even patterns having deterioration as an occlusion via image processing and machine learning methods. A challenging issue that researchers would tackle in detecting historical geometric pattern’s types is the variety in geometric textures, especially when they have occlusion such as deterioration. This issue leads to limited success in classifying via extracting only one feature. The other issue is that the extracted feature must be invariant to the transformation, such as scale, rotation, and noise variation. To cope with the challenges mentioned above and accurately classify, we plan to use the fusion method based on extracting global and local features. So, the features extracted from images in this research are based on local and global. In other words, the proposed fusion strategy lies both in feature and decision level, but the core is the proposed three combination methods in fusion decision methods. In this method, the dataset is composed of four main Persian geometric pattern types: Tond dah, Kond tablghenas, Hashtva 4 lengeh, and HashtvatablKond. So, the model will be trained by extracting global and local features of the images separately. Random forest, as the prevalent machine learning algorithm, is proposed for training data and predicting the class of input images. Finally, the probability of prediction for random forest classifiers is fused by the Decision Templates (DT) combiner, Naïve Bayes (NB) combiner, and Dempster–Shafer combination methods. In comparison with the individual classifier accuracy results of 80% and 85% for global and local features respectively, our proposed approach achieves an improved accuracy of 90%, 88%, and 90% in three fusion decision methods including DT combiner, NB combiner, and Dempster–Shafer combination methods.

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Change history

  • 28 June 2024

    The original online version of this article was revised to include the ORCID for the author Bita Hajebi.

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Acknowledgements

This project was funded by Iran Science Elites Federation. The authors are thankful to Dr. Leily A.Bakhtiar for her support and critical comments regarding this article.

Author information

Author notes

  1. Bita Hajebi and Pooya Hajebi contributed equally to this work.

Authors and Affiliations

  1. Department of Architectural and Urban Conservation, Art University of Isfahan, Hakim Nezami, Isfahan, 8175894418, Esfahan, Iran

    Bita Hajebi

  2. Department of Electrical Engineering, Yazd University, University Boulevard, Yazd, 8915818411, Yazd, Iran

    Pooya Hajebi

  3. Department of Mechanical Engineering, Isfahan University of Technology, Isfahan, 8415683111, Iran

    Pooya Hajebi

Authors
  1. Bita Hajebi
  2. Pooya Hajebi

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Correspondence toPooya Hajebi.

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Hajebi, B., Hajebi, P. Improving recognition of deteriorated historical Persian geometric patterns by fusion decision methods.Neural Comput & Applic36, 11809–11831 (2024). https://doi.org/10.1007/s00521-024-09932-3

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