24 Haziran 2024 Konferans bildirisi Açık Erişim

Kevseroğlu, Özlem; Kurban, Rifat

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    "creators": [
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        "affiliation": "Abdullah G\u00fcl \u00dcniversitesi", 
        "name": "Kevsero\u011flu, \u00d6zlem", 
        "orcid": "0000-0003-1828-2256"
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        "affiliation": "Abdullah G\u00fcl \u00dcniversitesi", 
        "name": "Kurban, Rifat", 
        "orcid": "0000-0002-0277-2210"
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    "description": "<p>The categorization of images captured during the documentation of architectural structures is a crucial aspect of preserving cultural heritage in digital form. Dealing with a large volume of images makes this categorization process laborious and time-consuming, often leading to errors. Introducing automatic techniques to aid in sorting would streamline this process, enhancing the efficiency of digital documentation. Proper classification of these images facilitates improved organization and more effective searches using specific terms, thereby aiding in the analysis and interpretation of the heritage asset. This study primarily focuses on applying deep learning techniques, specifically SqueezeNet convolutional neural networks (CNNs), for classifying images of architectural heritage. The effectiveness of training these networks from scratch versus fine-tuning pre-existing models is examined. In this study, we concentrate on identifying significant elements within images of buildings with architectural heritage significance of Kayseri Culture Route. Since no suitable datasets for network training were found, a new dataset was created. Transfer learning enables the use of pre-trained convolutional neural networks to specific image classification tasks. In the experiments, 99.8% of classification accuracy have been achieved by using SqueezeNet, suggesting that the implementation of the technique can substantially enhance the digital documentation of architectural heritage.</p>", 
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    "keywords": [
      "Convolutional neural networks", 
      "Deep learning", 
      "SqueezeNet", 
      "Cultural Heritage", 
      "Image Classification"
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    "language": "eng", 
    "license": {
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    "meeting": {
      "acronym": "AICCONF '24", 
      "dates": "25 May\u0131s 2024", 
      "place": "\u0130stanbul, T\u00fcrkiye", 
      "title": "Proceedings of the Cognitive Models and Artificial Intelligence Conference"
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      "title": "Konferans bildirisi", 
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    "science_branches": [
      "Teknik Bilimler > Bilgisayar Bilimleri", 
      "Teknik Bilimler > Bilgisayar Bilimleri > Yapay Zeka, Bilgisayarda \u00d6\u011frenme ve \u00d6r\u00fcnt\u00fc Tan\u0131ma > Bilgisayar \u00d6\u011frenimi", 
      "Teknik Bilimler > Mimarl\u0131k > \u015eehir ve B\u00f6lge Planlama > Kent Planlamas\u0131 ve Geli\u015fimi"
    ], 
    "title": "Re-exploring the Kayseri Culture Route by Using Deep Learning for Cultural Heritage Image Classification"
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    2223
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