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dc.contributor.authorKevseroğlu, Ozlem
dc.contributor.authorKurban, Rifat
dc.date.accessioned2024-08-29T11:25:28Z
dc.date.available2024-08-29T11:25:28Z
dc.date.issued2024en_US
dc.identifier.isbn979-840071692-8
dc.identifier.urihttps://doi.org/10.1145/3660853.3660913
dc.identifier.urihttps://hdl.handle.net/20.500.12573/2360
dc.description.abstractThe 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.en_US
dc.language.isoengen_US
dc.publisherAssociation for Computing Machineryen_US
dc.relation.isversionof10.1145/3660853.3660913en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectConvolutional neural networksen_US
dc.subjectDeep learningen_US
dc.subjectSqueezeNeten_US
dc.subjectCultural Heritageen_US
dc.subjectImage Classificationen_US
dc.titleRe-exploring the Kayseri Culture Route by Using Deep Learning for Cultural Heritage Image Classification Cultural Heritage Image Classification by Using Deep Learning: Kayseri Culture Routeen_US
dc.typeconferenceObjecten_US
dc.contributor.departmentAGÜ, Mimarlık Fakültesi, Mimarlık Bölümüen_US
dc.contributor.authorID0000-0003-1828-2256en_US
dc.contributor.authorID0000-0002-0277-2210en_US
dc.contributor.institutionauthorKevseroğlu, Ozlem
dc.contributor.institutionauthorKurban, Rifat
dc.identifier.startpage196en_US
dc.identifier.endpage201en_US
dc.relation.journalACM International Conference Proceeding Seriesen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US


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